REVIEW 3 major objections 5 minor 244 references
Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning
T0 review · 3 major / 5 minor · reviewed 2026-07-12 · grok-4.5
Pith's one-line read Natural captions are incomplete constraints: align vision to the consensus core, suppress residual over-commitment, and confidence follows how strongly text pins the image.
desk verdict Solid dual-encoder recipe that treats captions as partial constraints; gains look real, but synthetic multi-view construction is the soft underbelly. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
Core–residual alignment (TPC): consensus core ci as the normalized mean of multi-view text embeddings; single-view core filter ψθ; unsaid residual ri,k orthogonal to the predicted core; non-commitment loss that squares image–residual correlation; and uncertainty-aware InfoNCE with temperature τi = τ0(1 + γui).
What would settle it
Train and evaluate with only genuine multi-caption human data versus the paper’s synthesized-view recipe on single-caption corpora; if the clean/robust ImageNet and Avg-14 margins and residual-leakage reduction disappear when synthetic views are removed or replaced by unfiltered generation, the partial-constraint claim does not hold under realistic supervision.
Extended reading notes
Core claim
Treating multi-view natural captions as incomplete supervision—aligning images to a consensus semantic core, predicting that core from a single caption at test time, suppressing correlation with the orthogonal unsaid residual, and scaling contrastive temperature by caption-view disagreement—yields vision–language representations whose matching is more stable under paraphrase and whose confidence better tracks how strongly text constrains the image, with measured gains on zero-shot recognition, adversarial robustness, and LVLM transfer.
Load-bearing premise
The normalized average of several captions of the same image is a faithful target for the shared meaning, and the extra captions built for single-caption web data (shortened cores, paraphrases, back-translations) truly expose underspecification rather than train on artifacts.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes Text as Partial Constraint (TPC), a core–residual vision–language alignment framework that treats multi-view captions as incomplete supervision. It defines a multi-view consensus core ci (Eq. 2) as the alignment target, trains a linear single-view core filter ψθ (Eq. 4) for one-text inference, penalizes image–residual correlation via Lnc (Eqs. 5–6), and softens InfoNCE with disagreement-based temperature τi=τ0(1+γui) (Eqs. 3, 7). Theory motivates residual non-commitment as a DRO-style worst-case bound (Thm. 5.1) and multi-view consensus as denoising (Thm. 5.2). Empirically, TPC reports 81.42/64.05 ImageNet clean/robust Top-1, 76.19/52.03 on Avg-14, and improved LLaVA-1.5-7B transfer (85.16 POPE F1, 59.57 OKVQA), with ablations, residual-leakage, risk–coverage, caption-sensitivity, controlled-deletion, and backbone analyses.
Significance. If the gains isolate the partial-constraint principle rather than multi-text augmentation, the work is a clear and practical contribution to robust open-vocabulary alignment and LVLM grounding under underspecified language. Strengths include a diagnostic preliminary study (Sec. 3), single-text deployability via ψθ, multi-front empirical stress tests (residual leakage Fig. 5, risk–coverage Fig. 6, controlled deletion Fig. 11, caption-choice sensitivity Fig. 7), seed variability (Table 5), and compute disclosure. The theory is motivational rather than machine-checked, but Thm. 5.1 cleanly links core alignment and residual sensitivity to worst-case similarity. The main significance risk is whether headline numbers credit core–residual alignment or simply richer multi-view text supervision.
major comments (3)
- [§6.2–6.3, Tables 1–3] The central claim that gains come from modeling text as a partial constraint is not isolated from multi-text augmentation. Table 3 removes TPC components but does not include a matched multi-view baseline that uses the same K captions (human or synthetic) with standard multi-positive contrastive loss and no core/residual machinery. Without that control, the ImageNet/Avg-14/LVLM improvements (Tables 1–2) may partly reflect extra text views rather than consensus-core alignment and residual non-commitment (Eqs. 6–9).
- [Appendix B.2; §5.2, Thm. 5.2; Eqs. 2–6] Appendix B.2 synthesizes views for single-caption web data via core-only deletion, paraphrase, and back-translation with a weak frozen-encoder filter (cosine ≥0.25). Thm. 5.2 models views as zero-mean noise around a latent core μi, but systematic deletion removes structure rather than adding noise, so the denoising justification does not transfer to the large-scale regime that drives dual-encoder training. The paper should either (i) report human-multi-caption-only vs. synthetic-view training splits for residual leakage and main metrics, or (ii) substantially strengthen faithfulness checks and explicitly limit the theory’s scope.
- [§6.1, Table 1; Appendix B.4–B.5] Training data composition, step budget, and compute matching for the headline ImageNet/Avg-14 numbers versus robust baselines (TeCoA, FARE, Adv-W2S) remain underspecified beyond “match when feasible” (§6.1, B.4–B.5). Because adversarial methods incur different costs and TPC’s multi-view text path changes supervision density, a compute- and data-matched block (or clear external-checkpoint labeling) is needed before attributing +5–7 point robust gains primarily to core–residual alignment.
minor comments (5)
- [Table 1; Appendix B.3] Adversarial radius is inconsistent: Table 1 caption states AutoAttack ϵ=2/255, while Appendix B.3 defaults to ϵ=4/255. Please unify the protocol and restate which radius was used for all methods.
- [Abstract; §1; §4; §6] Title/method casing is broken in several places (“TEXT ASPARTIAL CONSTRAINT”, “TEXT ASPARTIALCONSTRAIN”). Normalize to “Text as Partial Constraint (TPC)” throughout.
- [Fig. 4] Figure 4 axis labels show encoding artifacts (“/glyph1197umber of views”, “/glyph1197on-com m itm ent”). Clean the PDF text layer.
- [§2] Related Work cites three concurrent ICLR 2026 papers (Xiao et al., Zhang et al., Zhou et al.) as “adjacent”; a one-sentence differentiation of partial-constraint alignment vs. continual/compositional adaptation would help readers.
- [§4.6; §6.1] In §4.6, state explicitly that τ(y) calibration preserves ranking while only changing softmax sharpness; this is said once but should be repeated near the LVLM transfer protocol so readers do not confuse confidence calibration with re-ranking.
Circularity Check
No significant circularity: TPC is a standard method+benchmark paper; theory motivates objectives and reported metrics are external evaluations, not quantities forced by construction.
full rationale
Walking the load-bearing chain: (i) the consensus core ci (Eq. 2), residual ri,k (Eq. 5), Lnc (Eq. 6), Lcore (Eq. 7), and Lagree (Eq. 8) are design choices that define the training objective, not claimed first-principles predictions of external observables; (ii) Theorems 5.1–5.2 are motivational DRO/PAC analyses showing that worst-case similarity under a residual ambiguity set improves by raising core alignment and lowering residual sensitivity, and that multi-view averaging reduces noise—standard justification of the chosen losses, not identities that make the reported accuracies true by definition; (iii) headline numbers (ImageNet clean/robust, Avg-14, POPE, OKVQA, COCO/Flickr) are measured on external held-out benchmarks against independent baselines; (iv) hyperparameter tuning of γ, λnc, λagree, τ0, K on validation is ordinary ML practice, not renaming a fit as a prediction; (v) self-citations (e.g., Xiao/Zhang/Zhou 2026) appear only as adjacent continual-learning work and are not load-bearing uniqueness or ansatz imports for TPC. Weaknesses about synthetic multi-view construction (Appendix B.2) and whether ci is a faithful latent core are assumption/correctness risks, not circular reductions of outputs to inputs. Score 0 with empty steps is therefore the honest finding.
Assumptions & free parameters
free parameters (6)
- base temperature τ0 =
0.07
- uncertainty scale γ =
1.0
- non-commitment weight λnc =
0.1
- core agreement weight λagree =
1.0
- number of caption views K =
4–5
- synthetic-view cosine filter threshold =
≥0.25
assumptions (6)
- domain assumption Multiple textual views of an image share a stable semantic core while differing in omitted or ambiguous details.
- ad hoc to paper Components of a caption embedding orthogonal to the (predicted) core are unsaid residuals that should not drive image–text similarity.
- ad hoc to paper Caption-view disagreement ui is a monotone proxy for language underspecification and should soften contrastive updates.
- domain assumption Hemisphere condition t⊤i,k ci ≥ 0 for observed views so that residual radius and ambiguity set A_i are well-defined.
- domain assumption Multi-view text embeddings are noisy observations of a latent unit core with bounded zero-mean noise (Eq. 14).
- standard math Standard dual-encoder cosine similarity and InfoNCE-style contrastive learning are appropriate for open-vocabulary transfer.
invented entities (4)
-
consensus semantic core ci
-
unsaid residual ri,k
-
single-view core filter ψθ
-
caption-view disagreement uncertainty ui / residual energy û(y)
Cite this review
Pith. "Pith review of Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning." pith.science (2026). https://pith.science/paper/D4AXM4J3
@misc{pith2026260703143,
author = {Pith},
title = {Pith review of: Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning},
year = {2026},
howpublished = {\url{https://pith.science/paper/D4AXM4J3}},
note = {Machine review of arXiv:2607.03143}
}
read the original abstract
Vision-language alignment powers open-vocabulary recognition, retrieval, and LVLM grounding, yet natural captions are often underspecified, making similarity brittle and overly confident under paraphrase and omitted details. We aim to learn representations whose matching is stable across caption views and whose confidence reflects how strongly text constrains an image. We propose Text as Partial Constraint (TPC), a core-residual alignment framework that treats multi-view captions as incomplete supervision. It distills a consensus semantic core as the alignment target, learns a single-view core predictor for standard inference with one query, and explicitly discourages vision-language similarity from depending on the orthogonal unsaid residual. An uncertainty-aware contrastive objective further softens alignment when caption views disagree, reducing overconfident updates under weak language constraints. Across zero-shot recognition and adversarial robustness, TPC achieves 81.42/64.05 Top-1 clean/robust accuracy on ImageNet and 76.19/52.03 on an Avg-14 transfer suite, while improving LVLM transfer with 85.16 POPE F1 and 59.57 OKVQA accuracy under an LLaVA-1.5-7B stack. These results suggest that modeling text as a partial constraint is a practical and principled route to more reliable vision-language representations under underspecified language supervision.
Figures
Figures from the paper (8 more)
Reference graph
Works this paper leans on
-
[1]
The Fourteenth International Conference on Learning Representations , year=
Reversible primitive--composition alignment for continual vision--language learning , author=. The Fourteenth International Conference on Learning Representations , year=
-
[2]
The Fourteenth International Conference on Learning Representations , year=
Pi-cca: Prompt-invariant cca certificates for replay-free continual multimodal learning , author=. The Fourteenth International Conference on Learning Representations , year=
-
[3]
The Fourteenth International Conference on Learning Representations , year=
Comem: Compositional concept-graph memory for vision--language adaptation , author=. The Fourteenth International Conference on Learning Representations , year=
-
[4]
Langley , title =
P. Langley , title =. Proceedings of the 17th International Conference on Machine Learning (ICML 2000) , address =. 2000 , pages =
2000
-
[5]
Proceedings of the 41st International Conference on Machine Learning , pages=
Robust CLIP: unsupervised adversarial fine-tuning of vision embeddings for robust large vision-language models , author=. Proceedings of the 41st International Conference on Machine Learning , pages=
-
[6]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Improved baselines with visual instruction tuning , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[7]
International conference on machine learning , pages=
Scaling up visual and vision-language representation learning with noisy text supervision , author=. International conference on machine learning , pages=. 2021 , organization=
2021
-
[8]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Mitigating object hallucinations in large vision-language models through visual contrastive decoding , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Show all 244 references
-
[9]
Advances in Neural Information Processing Systems , volume=
Text-guided attention is all you need for zero-shot robustness in vision-language models , author=. Advances in Neural Information Processing Systems , volume=
-
[10]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Pre-trained model guided fine-tuning for zero-shot adversarial robustness , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[11]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Winoground: Probing vision and language models for visio-linguistic compositionality , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[12]
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
VALSE: A task-independent benchmark for vision and language models centered on linguistic phenomena , author=. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[13]
arXiv preprint arXiv:2601.06165 , year=
What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models , author=. arXiv preprint arXiv:2601.06165 , year=
-
[14]
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
Vision-language models do not understand negation , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
-
[15]
Advances in Neural Information Processing Systems , volume=
Sugarcrepe++ dataset: Vision-language model sensitivity to semantic and lexical alterations , author=. Advances in Neural Information Processing Systems , volume=
-
[16]
Advances in neural information processing systems , volume=
Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality , author=. Advances in neural information processing systems , volume=
-
[17]
arXiv preprint arXiv:2508.14264 , year=
Directed-Tokens: A Robust Multi-Modality Alignment Approach to Large Language-Vision Models , author=. arXiv preprint arXiv:2508.14264 , year=
-
[18]
arXiv preprint arXiv:2505.17316 , year=
Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models , author=. arXiv preprint arXiv:2505.17316 , year=
-
[19]
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=
Evaluating Object Hallucination in Large Vision-Language Models , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=
2023
-
[20]
arXiv preprint arXiv:2308.01390 , year=
Openflamingo: An open-source framework for training large autoregressive vision-language models , author=. arXiv preprint arXiv:2308.01390 , year=
-
[21]
Advances in neural information processing systems , volume=
Visual instruction tuning , author=. Advances in neural information processing systems , volume=
-
[22]
arXiv preprint arXiv:2510.21323 , year=
VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set , author=. arXiv preprint arXiv:2510.21323 , year=
-
[23]
The Eleventh International Conference on Learning Representations , year =
Understanding Zero-shot Adversarial Robustness for Large-Scale Models , author=. The Eleventh International Conference on Learning Representations , year =
-
[24]
arXiv preprint arXiv:2303.15389 , year=
Eva-clip: Improved training techniques for clip at scale , author=. arXiv preprint arXiv:2303.15389 , year=
-
[25]
Proceedings of the IEEE/CVF international conference on computer vision , pages=
Sigmoid loss for language image pre-training , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=
-
[26]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Reproducible scaling laws for contrastive language-image learning , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[27]
International conference on machine learning , pages=
Learning transferable visual models from natural language supervision , author=. International conference on machine learning , pages=. 2021 , organization=
2021
-
[28]
Proceedings of the AAAI conference on artificial intelligence , volume=
Are transformers effective for time series forecasting? , author=. Proceedings of the AAAI conference on artificial intelligence , volume=
-
[29]
International Conference on Machine Learning , pages=
MOMENT: A Family of Open Time-series Foundation Models , author=. International Conference on Machine Learning , pages=. 2024 , organization=
2024
-
[30]
Timer-XL: Long-Context Transformers for Unified Time Series Forecasting , author=
-
[31]
ICLR 2025: The Thirteenth International Conference on Learning Representations , year=
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts , author=. ICLR 2025: The Thirteenth International Conference on Learning Representations , year=
2025
-
[32]
arXiv preprint arXiv:2507.14507 , year=
Diffusion models for time series forecasting: A survey , author=. arXiv preprint arXiv:2507.14507 , year=
-
[33]
International conference on machine learning , pages=
Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting , author=. International conference on machine learning , pages=. 2021 , organization=
2021
-
[34]
Advances in neural information processing systems , volume=
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting , author=. Advances in neural information processing systems , volume=
-
[35]
Proceedings of the AAAI conference on artificial intelligence , volume=
Informer: Beyond efficient transformer for long sequence time-series forecasting , author=. Proceedings of the AAAI conference on artificial intelligence , volume=
-
[36]
The eleventh international conference on learning representations , year=
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting , author=. The eleventh international conference on learning representations , year=
-
[37]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Construct-vl: Data-free continual structured vl concepts learning , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[38]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Continual learning for visual search with backward consistent feature embedding , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[39]
Advances in neural information processing systems , volume=
Dark experience for general continual learning: a strong, simple baseline , author=. Advances in neural information processing systems , volume=
-
[40]
Workshop on Multi-Task and Lifelong Reinforcement Learning , year=
Continual learning with tiny episodic memories , author=. Workshop on Multi-Task and Lifelong Reinforcement Learning , year=
-
[41]
IEEE Transactions on Instrumentation and Measurement , year=
GALMOR: Memory-Constrained Continual Learning With Efficient Replay for Fault Diagnosis of Rotating Machinery , author=. IEEE Transactions on Instrumentation and Measurement , year=
-
[42]
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
Guilin Zhu and Dongyue Wu and Changxin Gao and Runmin Wang and Weidong Yang and Nong Sang , title =. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
-
[43]
arXiv preprint arXiv:2503.06683 , year =
Dynamic Dictionary Learning for Remote Sensing Image Segmentation , author =. arXiv preprint arXiv:2503.06683 , year =
-
[44]
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , year =
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation , author =. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , year =
-
[45]
arXiv preprint arXiv:2501.13925 , year =
GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing , author =. arXiv preprint arXiv:2501.13925 , year =
-
[46]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images , author =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
-
[47]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling , author =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
-
[48]
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation , author =. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
-
[49]
IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=
Learning at a glance: Towards interpretable data-limited continual semantic segmentation via semantic-invariance modelling , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=. 2024 , publisher=
2024
-
[50]
IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium , pages=
Self-training and curriculum learning guided dynamic refined network for remote sensing class-incremental semantic segmentation , author=. IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium , pages=. 2024 , month=
2024
-
[51]
IEEE Transactions on Geoscience and Remote Sensing , volume=
Domain-Incremental Learning for Remote Sensing Semantic Segmentation With Multifeature Constraints in Graph Space , author=. IEEE Transactions on Geoscience and Remote Sensing , volume=. 2024 , publisher=
2024
-
[52]
Science China Information Sciences , volume=
Mitigating representation bias for class-incremental semantic segmentation of remote sensing images , author=. Science China Information Sciences , volume=. 2025 , doi=
2025
-
[53]
IEEE Transactions on Geoscience and Remote Sensing , volume=
MiSSNet: Memory-inspired semantic segmentation augmentation network for class-incremental learning in remote sensing images , author=. IEEE Transactions on Geoscience and Remote Sensing , volume=. 2024 , publisher=
2024
-
[54]
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
Yirui Wu and Yuhang Xia and Hao Li and Lixin Yuan and Junyang Chen and Jun Liu and Tong Lu and Shaohua Wan , title =. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
-
[55]
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
Zhidong Yu and Xiaoman Liu and Jiajun Hu and Zhenbo Shi and Wei Yang , title =. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
-
[56]
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
Cheng Xu and Weiwen Zhang and Hongrui Zhang and Xuemiao Xu and Huaidong Zhang and Jing Zou and Jing Qin , title =. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =
-
[57]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[58]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
Hongmei Yin and Tingliang Feng and Fan Lyu and Fanhua Shang and Hongying Liu and Wei Feng and Liang Wan , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
-
[59]
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , volume=
Understanding video events: A survey of methods for automatic interpretation of semantic occurrences in video , author=. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) , volume=. 2009 , publisher=
2009
-
[60]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Revisiting the" video" in video-language understanding , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[61]
Advances in Neural Information Processing Systems , volume=
A Practitioner's Guide to Real-World Continual Multimodal Pretraining , author=. Advances in Neural Information Processing Systems , volume=
-
[62]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
Yuchen Zhu and Cheng Shi and Dingyou Wang and Jiajin Tang and Zhengxuan Wei and Yu Wu and Guanbin Li and Sibei Yang , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
-
[63]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
Kai Fang and Anqi Zhang and Guangyu Gao and Jianbo Jiao and Chi Harold Liu and Yunchao Wei , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , year =
-
[64]
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , month =
Maoxian Wan and Kaige Li and Qichuan Geng and Weimin Shi and Zhong Zhou , title =. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , month =. 2025 , pages =
2025
-
[65]
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , month =
Ruitao Wu and Yifan Zhao and Jia Li , title =. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , month =. 2025 , pages =
2025
-
[66]
Advances in Neural Information Processing Systems , year=
OVS Meets Continual Learning: Towards Sustainable Open-Vocabulary Segmentation , author=. Advances in Neural Information Processing Systems , year=
-
[67]
Advances in Neural Information Processing Systems , year=
Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation , author=. Advances in Neural Information Processing Systems , year=
-
[68]
Advances in Neural Information Processing Systems , year=
Open-Vocabulary Part Segmentation via Progressive and Boundary-Aware Strategy , author=. Advances in Neural Information Processing Systems , year=
-
[69]
Advances in Neural Information Processing Systems , year=
Seg4Diff: Unveiling Open-Vocabulary Segmentation in Text-to-Image Diffusion Transformers , author=. Advances in Neural Information Processing Systems , year=
-
[70]
Advances in Neural Information Processing Systems , year=
OPMapper: Enhancing Open-Vocabulary Semantic Segmentation with Multi-Guidance Information , author=. Advances in Neural Information Processing Systems , year=
-
[71]
Advances in Neural Information Processing Systems , year=
LangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation , author=. Advances in Neural Information Processing Systems , year=
-
[72]
Advances in Neural Information Processing Systems , year=
Continual Gaussian Mixture Distribution Modeling for Class Incremental Semantic Segmentation , author=. Advances in Neural Information Processing Systems , year=
-
[73]
Advances in Neural Information Processing Systems , year=
Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation , author=. Advances in Neural Information Processing Systems , year=
-
[74]
Forty-second International Conference on Machine Learning , year=
Divide and Conquer: Exploring Language-centric Tree Reasoning for Video Question-Answering , author=. Forty-second International Conference on Machine Learning , year=
-
[75]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
VideoLLaMB: Long Streaming Video Understanding with Recurrent Memory Bridges , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[76]
arXiv preprint arXiv:2507.00469 , year=
Bisecle: Binding and Separation in Continual Learning for Video Language Understanding , author=. arXiv preprint arXiv:2507.00469 , year=
-
[77]
Proceedings of the 32nd ACM International Conference on Multimedia , pages=
Gpt4video: A unified multimodal large language model for lnstruction-followed understanding and safety-aware generation , author=. Proceedings of the 32nd ACM International Conference on Multimedia , pages=
-
[78]
2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pages=
Dam: Dynamic adapter merging for continual video qa learning , author=. 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , pages=. 2025 , organization=
2025
-
[79]
arXiv preprint arXiv:2502.00843 , year=
VLM-assisted continual learning for visual question answering in self-driving , author=. arXiv preprint arXiv:2502.00843 , year=
-
[80]
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[81]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Feature Decomposition-Recomposition in Large Vision-Language Model for Few-Shot Class-Incremental Learning , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[82]
arXiv preprint arXiv:2410.17434 , year=
Longvu: Spatiotemporal adaptive compression for long video-language understanding , author=. arXiv preprint arXiv:2410.17434 , year=
-
[83]
arXiv preprint arXiv:2503.14963 , year=
Continual multimodal contrastive learning , author=. arXiv preprint arXiv:2503.14963 , year=
-
[84]
IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
Learning without forgetting for vision-language models , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
-
[85]
International conference on machine learning , pages=
Open-vclip: Transforming clip to an open-vocabulary video model via interpolated weight optimization , author=. International conference on machine learning , pages=. 2023 , organization=
2023
-
[86]
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=
Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=
2024
-
[87]
Advances in Neural Information Processing Systems , volume=
BMU-MoCo: Bidirectional momentum update for continual video-language modeling , author=. Advances in Neural Information Processing Systems , volume=
-
[88]
Advances in Neural Information Processing Systems , volume=
Vilco-bench: Video language continual learning benchmark , author=. Advances in Neural Information Processing Systems , volume=
-
[89]
arXiv preprint arXiv:2508.04227 , year=
Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting , author=. arXiv preprint arXiv:2508.04227 , year=
-
[90]
Forty-second International Conference on Machine Learning , year=
Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning , author=. Forty-second International Conference on Machine Learning , year=
-
[91]
International conference on machine learning , pages=
Deep canonical correlation analysis , author=. International conference on machine learning , pages=. 2013 , organization=
2013
-
[92]
arXiv preprint arXiv:2110.08733 , year=
LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation , author=. arXiv preprint arXiv:2110.08733 , year=
-
[93]
IEEE Transactions on Geoscience and Remote Sensing , volume=
Historical information-guided class-incremental semantic segmentation in remote sensing images , author=. IEEE Transactions on Geoscience and Remote Sensing , volume=. 2022 , publisher=
2022
-
[94]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , volume=
Automated high-resolution earth observation image interpretation: Outcome of the 2020 Gaofen challenge , author=. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , volume=. 2021 , publisher=
2020
-
[95]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages=
isaid: A large-scale dataset for instance segmentation in aerial images , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages=
-
[96]
Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers , pages=
Large-scale machine learning with stochastic gradient descent , author=. Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers , pages=. 2010 , organization=
2010
-
[97]
ISPRS: Leopoldsh
ISPRS semantic labeling contest , author=. ISPRS: Leopoldsh
-
[98]
Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pages=
Deepglobe 2018: A challenge to parse the earth through satellite images , author=. Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pages=
2018
-
[99]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Dynamic Multi-Layer Null Space Projection for Vision-Language Continual Learning , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[100]
Automation in Construction , volume=
Context-aware vision-language model agent enriched with domain-specific ontology for construction site safety monitoring , author=. Automation in Construction , volume=. 2025 , publisher=
2025
-
[101]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Overcoming Dual Drift for Continual Long-Tailed Visual Question Answering , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[102]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Pretrained language models as visual planners for human assistance , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[103]
Proceedings of the Nineteenth ACM Conference on Recommender Systems , pages=
Improving Visual Recommendation on E-commerce Platforms Using Vision-Language Models , author=. Proceedings of the Nineteenth ACM Conference on Recommender Systems , pages=
-
[104]
IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
Foundation models defining a new era in vision: a survey and outlook , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
-
[105]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Maple: Multi-modal prompt learning , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[106]
International Journal of Computer Vision , volume=
Learning to prompt for vision-language models , author=. International Journal of Computer Vision , volume=. 2022 , publisher=
2022
-
[107]
Advances in neural information processing systems , volume=
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability , author=. Advances in neural information processing systems , volume=
-
[108]
International conference on machine learning , pages=
Similarity of neural network representations revisited , author=. International conference on machine learning , pages=. 2019 , organization=
2019
-
[109]
Advances in neural information processing systems , volume=
Insights on representational similarity in neural networks with canonical correlation , author=. Advances in neural information processing systems , volume=
-
[110]
Advances in Neural Information Processing Systems , volume=
Ratt: Recurrent attention to transient tasks for continual image captioning , author=. Advances in Neural Information Processing Systems , volume=
-
[111]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=
Visually Grounded Continual Learning of Compositional Phrases , author=. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages=
2020
-
[112]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=
Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering , author=. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=
-
[113]
European conference on computer vision , pages=
Mind the interference: Retaining pre-trained knowledge in parameter efficient continual learning of vision-language models , author=. European conference on computer vision , pages=. 2024 , organization=
2024
-
[114]
European Conference on Computer Vision , pages=
Select and distill: Selective dual-teacher knowledge transfer for continual learning on vision-language models , author=. European Conference on Computer Vision , pages=. 2024 , organization=
2024
-
[115]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Principles of forgetting in domain-incremental semantic segmentation in adverse weather conditions , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[116]
Proceedings of the IEEE/CVF international conference on computer vision workshops , pages=
Incremental learning techniques for semantic segmentation , author=. Proceedings of the IEEE/CVF international conference on computer vision workshops , pages=
-
[117]
European conference on computer vision , pages=
Early preparation pays off: New classifier pre-tuning for class incremental semantic segmentation , author=. European conference on computer vision , pages=. 2024 , organization=
2024
-
[118]
European Conference on Computer Vision , pages=
Rbc: Rectifying the biased context in continual semantic segmentation , author=. European Conference on Computer Vision , pages=. 2022 , organization=
2022
-
[119]
Advances in neural information processing systems , volume=
Decomposed knowledge distillation for class-incremental semantic segmentation , author=. Advances in neural information processing systems , volume=
-
[120]
IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=
Uncertainty-aware contrastive distillation for incremental semantic segmentation , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=. 2022 , publisher=
2022
-
[121]
Advances in Neural Information Processing Systems , volume=
Samrs: Scaling-up remote sensing segmentation dataset with segment anything model , author=. Advances in Neural Information Processing Systems , volume=
-
[122]
2011 , publisher=
Introduction to remote sensing , author=. 2011 , publisher=
2011
-
[123]
International conference on medical imaging with deep learning , pages=
Boundary loss for highly unbalanced segmentation , author=. International conference on medical imaging with deep learning , pages=. 2019 , organization=
2019
-
[124]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Comformer: Continual learning in semantic and panoptic segmentation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[125]
IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
Replay master: Automatic sample selection and effective memory utilization for continual semantic segmentation , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=
-
[126]
Proceedings of the IEEE/CVF international conference on computer vision , pages=
Recall: Replay-based continual learning in semantic segmentation , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=
-
[127]
Advances in neural information processing systems , volume=
Ssul: Semantic segmentation with unknown label for exemplar-based class-incremental learning , author=. Advances in neural information processing systems , volume=
-
[128]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Modeling the background for incremental learning in semantic segmentation , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[129]
International conference on machine learning , pages=
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks , author=. International conference on machine learning , pages=. 2018 , organization=
2018
-
[130]
British Journal of Mathematical and Statistical Psychology , volume=
Distributions of the Kullback--Leibler divergence with applications , author=. British Journal of Mathematical and Statistical Psychology , volume=. 2011 , publisher=
2011
-
[131]
CoRR , year=
Moelora: An moe-based parameter efficient fine-tuning method for multi-task medical applications , author=. CoRR , year=
-
[132]
Advances in Neural Information Processing Systems , volume=
Coin: A benchmark of continual instruction tuning for multimodel large language models , author=. Advances in Neural Information Processing Systems , volume=
-
[133]
Advances in neural information processing systems , volume=
Gradient surgery for multi-task learning , author=. Advances in neural information processing systems , volume=
-
[134]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Incrementer: Transformer for class-incremental semantic segmentation with knowledge distillation focusing on old class , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[135]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Plop: Learning without forgetting for continual semantic segmentation , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[136]
Advances in Neural Information Processing Systems , volume=
Advancing cross-domain discriminability in continual learning of vision-language models , author=. Advances in Neural Information Processing Systems , volume=
-
[137]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Inflora: Interference-free low-rank adaptation for continual learning , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[138]
Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=
Orthogonal subspace learning for language model continual learning , author=. Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=
2023
-
[139]
Maharana, Adyasha and Yoon, Jaehong and Chen, Tianlong and Bansal, Mohit , booktitle=. Adapt-
-
[140]
Forty-second International Conference on Machine Learning , year=
SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning , author=. Forty-second International Conference on Machine Learning , year=
-
[141]
arXiv preprint arXiv:2506.02041 , year=
Enhancing Multimodal Continual Instruction Tuning with BranchLoRA , author=. arXiv preprint arXiv:2506.02041 , year=
-
[142]
arXiv preprint arXiv:2503.12941 , year=
Hide-llava: Hierarchical decoupling for continual instruction tuning of multimodal large language model , author=. arXiv preprint arXiv:2503.12941 , year=
-
[143]
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
Advancing smoe for continuous domain adaptation of mllms: Adaptive router and domain-specific loss , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[144]
arXiv preprint arXiv:2411.13949 , year=
Separable mixture of low-rank adaptation for continual visual instruction tuning , author=. arXiv preprint arXiv:2411.13949 , year=
-
[145]
International Conference on Learning Representations , year=
LoRA: Low-Rank Adaptation of Large Language Models , author=. International Conference on Learning Representations , year=
-
[146]
The Twelfth International Conference on Learning Representations , year =
TiC-CLIP: Continual Training of CLIP Models , author=. The Twelfth International Conference on Learning Representations , year =
-
[147]
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
-
[148]
arXiv preprint arXiv:2502.04469 , year=
Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering , author=. arXiv preprint arXiv:2502.04469 , year=
-
[149]
arXiv preprint arXiv:2507.09118 , year=
Mind the gap: Preserving and compensating for the modality gap in clip-based continual learning , author=. arXiv preprint arXiv:2507.09118 , year=
-
[150]
arXiv preprint arXiv:2503.08510 , year=
External Knowledge Injection for CLIP-Based Class-Incremental Learning , author=. arXiv preprint arXiv:2503.08510 , year=
-
[151]
Forty-second International Conference on Machine Learning , year =
LADA: Scalable Label-Specific CLIP Adapter for Continual Learning , author=. Forty-second International Conference on Machine Learning , year =
-
[152]
Forty-second International Conference on Machine Learning , year =
Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting , author=. Forty-second International Conference on Machine Learning , year =
-
[153]
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
Synthetic Data is an Elegant GIFT for Continual Vision-Language Models , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
-
[154]
The Thirteenth International Conference on Learning Representations , year=
C-clip: Multimodal continual learning for vision-language model , author=. The Thirteenth International Conference on Learning Representations , year=
-
[155]
Advances in Neural Information Processing Systems , volume=
Stabilizing zero-shot prediction: A novel antidote to forgetting in continual vision-language tasks , author=. Advances in Neural Information Processing Systems , volume=
-
[156]
Advances in neural information processing systems , volume=
Clap4clip: Continual learning with probabilistic finetuning for vision-language models , author=. Advances in neural information processing systems , volume=
-
[157]
International Conference on Machine Learning , pages=
Continual vision-language representation learning with off-diagonal information , author=. International Conference on Machine Learning , pages=. 2023 , organization=
2023
-
[158]
European Conference on Computer Vision , pages=
Generative negative text replay for continual vision-language pretraining , author=. European Conference on Computer Vision , pages=. 2022 , organization=
2022
-
[159]
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
Continual vision-language retrieval via dynamic knowledge rectification , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=
-
[160]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Boosting continual learning of vision-language models via mixture-of-experts adapters , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[161]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Decouple before interact: Multi-modal prompt learning for continual visual question answering , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[162]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Vqacl: A novel visual question answering continual learning setting , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[163]
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
Symbolic replay: Scene graph as prompt for continual learning on vqa task , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=
-
[164]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Ctp: Towards vision-language continual pretraining via compatible momentum contrast and topology preservation , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[165]
arXiv preprint arXiv:1807.03748 , year=
Representation learning with contrastive predictive coding , author=. arXiv preprint arXiv:1807.03748 , year=
-
[166]
Proceedings of the IEEE/CVF international conference on computer vision , pages=
Preventing zero-shot transfer degradation in continual learning of vision-language models , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=
-
[167]
Advances in Neural Information Processing Systems , volume=
Less-forgetting multi-lingual fine-tuning , author=. Advances in Neural Information Processing Systems , volume=
-
[168]
European Conference on Computer Vision , pages=
Scanreason: Empowering 3d visual grounding with reasoning capabilities , author=. European Conference on Computer Vision , pages=. 2024 , organization=
2024
-
[169]
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
Bliva: A simple multimodal llm for better handling of text-rich visual questions , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=
-
[170]
Proceedings of the 47th International ACM SIGIR conference on research and development in information retrieval , pages=
Ldre: Llm-based divergent reasoning and ensemble for zero-shot composed image retrieval , author=. Proceedings of the 47th International ACM SIGIR conference on research and development in information retrieval , pages=
-
[171]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
A unified continual learning framework with general parameter-efficient tuning , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[172]
The Eleventh International Conference on Learning Representations , year =
Progressive Prompts: Continual Learning for Language Models , author=. The Eleventh International Conference on Learning Representations , year =
-
[173]
Advances in Neural Information Processing Systems , volume=
S-prompts learning with pre-trained transformers: An occam's razor for domain incremental learning , author=. Advances in Neural Information Processing Systems , volume=
-
[174]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[175]
European conference on computer vision , pages=
Dualprompt: Complementary prompting for rehearsal-free continual learning , author=. European conference on computer vision , pages=. 2022 , organization=
2022
-
[176]
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Learning to prompt for continual learning , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
-
[177]
IEEE transactions on pattern analysis and machine intelligence , volume=
Learning without forgetting , author=. IEEE transactions on pattern analysis and machine intelligence , volume=. 2017 , publisher=
2017
-
[178]
Proceedings of the national academy of sciences , volume=
Overcoming catastrophic forgetting in neural networks , author=. Proceedings of the national academy of sciences , volume=. 2017 , publisher=
2017
-
[179]
ACM Transactions on Multimedia Computing, Communications and Applications , volume=
A survey on temporal sentence grounding in videos , author=. ACM Transactions on Multimedia Computing, Communications and Applications , volume=. 2023 , publisher=
2023
-
[180]
FirstName LastName , title =
-
[181]
FirstName Alpher , title =
-
[182]
Journal of Foo , volume = 13, number = 1, pages =
FirstName Alpher and FirstName Fotheringham-Smythe , title =. Journal of Foo , volume = 13, number = 1, pages =
-
[183]
Journal of Foo , volume = 14, number = 1, pages =
FirstName Alpher and FirstName Fotheringham-Smythe and FirstName Gamow , title =. Journal of Foo , volume = 14, number = 1, pages =
-
[184]
FirstName Alpher and FirstName Gamow , title =
-
[185]
2025 , booktitle=
Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task , author=. 2025 , booktitle=
2025
-
[186]
arXiv preprint arXiv:2504.21403 , year=
Static or Dynamic: Towards Query-Adaptive Token Selection for Video Question Answering , author=. arXiv preprint arXiv:2504.21403 , year=
-
[187]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Object-centric video question answering with visual grounding and referring , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[188]
Modularized Self-Reflected Video Reasoner for Multimodal LLM with Application to Video Question Answering , author=
-
[189]
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages=
Graph Prompts: Adapting Video Graph for Video Question Answering , author=. Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages=
-
[190]
Divide and Conquer: Exploring Language-centric Tree Reasoning for Video Question-Answering , author=
-
[191]
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
Egotextvqa: Towards egocentric scene-text aware video question answering , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
-
[192]
2025 , booktitle=
Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering , author=. 2025 , booktitle=
2025
-
[193]
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
Commonsense video question answering through video-grounded entailment tree reasoning , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
-
[194]
Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
Bimba: Selective-scan compression for long-range video question answering , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
-
[195]
2025 , booktitle=
Streaming Video Question-Answering with In-context Video KV-Cache Retrieval , author=. 2025 , booktitle=
2025
-
[196]
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
Granularity-Adaptive Spatial Evidence Tokenization for Video Question Answering , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=
-
[197]
Proceedings of the AAAI Conference on Artificial Intelligence , volume=
Assessing modality bias in video question answering benchmarks with multimodal large language models , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=
-
[198]
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Coin: A large-scale dataset for comprehensive instructional video analysis , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
-
[199]
The Thirty-ninth Annual Conference on Neural Information Processing Systems , year =
Mitigating Intra-and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year =
-
[200]
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
A Dual-Mind Framework for Strategic and Expressive Negotiation Agent , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[201]
Findings of the Association for Computational Linguistics: ACL 2023 , pages=
Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models , author=. Findings of the Association for Computational Linguistics: ACL 2023 , pages=
2023
-
[202]
Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=
Simulating before planning: Constructing intrinsic user world model for user-tailored dialogue policy planning , author=. Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=
-
[203]
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=
2024
-
[204]
arXiv preprint arXiv:2509.17459 , year=
PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents , author=. arXiv preprint arXiv:2509.17459 , year=
-
[205]
arXiv preprint arXiv:2305.10142 , year=
Improving language model negotiation with self-play and in-context learning from ai feedback , author=. arXiv preprint arXiv:2305.10142 , year=
-
[206]
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=
Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=
2023
-
[207]
The Twelfth International Conference on Learning Representations , year =
Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents , author=. The Twelfth International Conference on Learning Representations , year =
-
[208]
Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration , author=. Findings of the Association for Computational Linguistics: EMNLP 2023 , pages=
2023
-
[209]
Proceedings of the 41st International Conference on Machine Learning , pages=
Language agent tree search unifies reasoning, acting, and planning in language models , author=. Proceedings of the 41st International Conference on Machine Learning , pages=
-
[210]
Advances in neural information processing systems , volume=
Tree of thoughts: Deliberate problem solving with large language models , author=. Advances in neural information processing systems , volume=
-
[211]
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=
Decoupling Strategy and Generation in Negotiation Dialogues , author=. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=
2018
-
[212]
arXiv preprint arXiv:1706.05125 , year=
Deal or no deal? end-to-end learning for negotiation dialogues , author=. arXiv preprint arXiv:1706.05125 , year=
-
[213]
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
Personalizing Dialogue Agents: I have a dog, do you have pets too? , author=. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[214]
Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=
Zero-shot persuasive chatbots with LLM-generated strategies and information retrieval , author=. Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=
2024
-
[215]
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue , author=. Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Proc...
-
[216]
Let's make your request more persuasive: Modeling persuasive strategies via semi-supervised neural nets on crowdfunding platforms , author=. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno...
2019
-
[217]
Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
Mcl for mllms: Benchmarking forgetting in task-incremental multimodal learning , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=
-
[218]
arXiv preprint arXiv:2503.01887 , year=
When continue learning meets multimodal large language model: A survey , author=. arXiv preprint arXiv:2503.01887 , year=
-
[219]
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
Os agents: A survey on mllm-based agents for computer, phone and browser use , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[220]
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) , pages=
Mira: Empowering one-touch ai services on smartphones with mllm-based instruction recommendation , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) , pages=
-
[221]
Proceedings of the 33rd ACM International Conference on Multimedia , pages=
Vqa2: visual question answering for video quality assessment , author=. Proceedings of the 33rd ACM International Conference on Multimedia , pages=
-
[222]
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=
Large language models know what is key visual entity: An LLM-assisted multimodal retrieval for VQA , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=
2024
-
[223]
National Science Review , volume=
A survey on multimodal large language models , author=. National Science Review , volume=. 2024 , publisher=
2024
-
[224]
The Thirteenth International Conference on Learning Representations , year=
Staff: Speculative coreset selection for task-specific fine-tuning , author=. The Thirteenth International Conference on Learning Representations , year=
-
[225]
arXiv preprint arXiv:2402.02318 , year=
Diversity measurement and subset selection for instruction tuning datasets , author=. arXiv preprint arXiv:2402.02318 , year=
-
[226]
Foundations and Trends
Determinantal point processes for machine learning , author=. Foundations and Trends. 2012 , publisher=
2012
-
[227]
arXiv preprint arXiv:2203.08913 , year=
Memorizing transformers , author=. arXiv preprint arXiv:2203.08913 , year=
-
[228]
arXiv preprint arXiv:1911.00172 , year=
Generalization through memorization: Nearest neighbor language models , author=. arXiv preprint arXiv:1911.00172 , year=
1911 arXiv
-
[229]
International conference on machine learning , pages=
Improving language models by retrieving from trillions of tokens , author=. International conference on machine learning , pages=. 2022 , organization=
2022
-
[230]
Advances in neural information processing systems , volume=
Retrieval-augmented generation for knowledge-intensive nlp tasks , author=. Advances in neural information processing systems , volume=
-
[231]
Proceedings of the AAAI conference on artificial intelligence , volume=
Persuasive influence detection: The role of argument sequencing , author=. Proceedings of the AAAI conference on artificial intelligence , volume=
-
[232]
Proceedings of the 25th international conference on world wide web , pages=
Winning arguments: Interaction dynamics and persuasion strategies in good-faith online discussions , author=. Proceedings of the 25th international conference on world wide web , pages=
-
[233]
Language Models are Unsupervised Multitask Learners , author=
-
[234]
What makes a good conversation? How controllable attributes affect human judgments , author=. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages=
2019
-
[235]
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders , author=. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
-
[236]
Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies , pages=
A diversity-promoting objective function for neural conversation models , author=. Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies , pages=
2016
-
[237]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good , author=. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=
-
[238]
T. M. Mitchell. The Need for Biases in Learning Generalizations. 1980
1980
-
[239]
M. J. Kearns , title =
-
[240]
Machine Learning: An Artificial Intelligence Approach, Vol. I. 1983
1983
-
[241]
R. O. Duda and P. E. Hart and D. G. Stork. Pattern Classification. 2000
2000
-
[242]
Suppressed for Anonymity , author=
-
[243]
Newell and P
A. Newell and P. S. Rosenbloom. Mechanisms of Skill Acquisition and the Law of Practice. Cognitive Skills and Their Acquisition. 1981
1981
-
[244]
A. L. Samuel. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 1959
1959
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