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arxiv: 2606.02502 · v1 · pith:7CVI5SP3new · submitted 2026-06-01 · 💻 cs.CL

CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning

Jun-Tao Tang , Zhen-Hao Xie , Yu-Cheng Shi , Da-Wei Zhou This is my paper

Pith reviewed 2026-06-28 14:15 UTC · model grok-4.3

classification 💻 cs.CL
keywords continual learningmultimodal instruction tuningmixture of expertscatastrophic forgettingadaptive routingparameter efficiencycentroid routingorthogonality penalty
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The pith

CRAM uses centroid-guided routing and adaptive MoE to enable parameter-efficient continual instruction tuning in multimodal models without catastrophic forgetting.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Multimodal large language models face a dilemma in continual learning: sharing parameters across tasks causes forgetting of old capabilities, while dedicating separate modules for each task wastes parameters over many tasks. The paper proposes CRAM to resolve this by isolating task-specific patterns in independent modules and using adaptive-rank instantiation to add only the parameters needed to bridge capability gaps. Centroid-guided routing reuses existing experts for new tasks, and an orthogonality penalty ensures new updates stay task-specific. This setup is tested on diverse benchmarks where it outperforms prior methods. Readers care because it points toward scalable, long-term deployment of models that can keep acquiring new vision-language skills.

Core claim

By isolating task-specific patterns into independent modules, CRAM mitigates catastrophic forgetting across tasks. Adaptive-rank instantiation identifies the capability gap between existing expert capability and new task demands, dynamically allocating only the necessary parameters. Centroid-guided routing recognizes and activates existing experts' capabilities for stable reuse, while an orthogonality penalty confines new updates to task-specific directions, preventing re-learning general capability. Extensive experiments across diverse benchmarks consistently demonstrate its superiority over existing methods.

What carries the argument

Centroid-guided routing in an adaptive Mixture-of-Experts architecture with rank-based parameter allocation and orthogonality penalty.

If this is right

  • Models maintain performance on previous tasks while incorporating new ones over extended sequences.
  • Total parameters scale sublinearly with the number of tasks based on measured capability gaps.
  • Routing decisions allow reuse of prior experts without retraining them for new tasks.
  • Updates remain localized, preserving general multimodal capabilities learned earlier.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Deployment of such models in real-world settings could lower the frequency of full retraining cycles.
  • Similar mechanisms might help in other continual learning domains like robotics or pure language tasks.
  • The method implies that quantifying capability gaps at addition time can optimize resource use in expanding AI systems.

Load-bearing premise

Adaptive-rank instantiation can reliably identify the capability gap between existing experts and new task demands without requiring post-hoc tuning that affects the reported gains.

What would settle it

If experiments on extended task sequences show that CRAM either exhibits significant forgetting comparable to shared-parameter baselines or requires parameter counts similar to full per-task modules, the efficiency and stability claims would not hold.

Figures

Figures reproduced from arXiv: 2606.02502 by Da-Wei Zhou, Jun-Tao Tang, Yu-Cheng Shi, Zhen-Hao Xie.

Figure 1
Figure 1. Figure 1: Format-level interference. (a) Accuracy change relative to zero-shot for each train-test format￾pair. (b) Ratio of format wrong but semantically correct responses among errors. 2 4 10 20 30 40 Training progress (/k samples) 40 45 50 55 60 Score on Flickr30k (%) Zero-shot 41.44 Flickr30k-only 57.51 General visual capability reuse (+10.0) Task-specific visual knowledge gap (-6.1) During VizWiz training [PIT… view at source ↗
Figure 2
Figure 2. Figure 2: Transfer on Flickr30k during VizWiz training. [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of CRAM. CRAM decouples via (i) Semantic Group Routing that group instructions with semantic similarity to protect output conventions, (ii) Adaptive-Rank Instantiation that allocates parameters exclusively to capability gaps via orthogonality filtering, and (iii) Centroid-Guided Orthogonal Learning to stabilize expert activation while confining new updates to orthogonal subspaces. projector π(·), … view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of instruction embeddings on [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Adaptive rank allocation analysis. (a) Rank [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Parameter comparison on TriGap [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overlap between ∆W and a historical expert before and after orthogonality filtering. Each column of ∆W is colored by its projection ratio onto the column space of a reference historical expert. transfer. This confirms that while the model is gen￾uinely acquiring instruction-following capabilities, its output behavior remains highly sensitive to sur￾face format conventions. Rigid syntactic templates are thu… view at source ↗
Figure 9
Figure 9. Figure 9: Training time comparison between Baseline [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
read the original abstract

Multimodal Large Language Models (MLLMs) unify heterogeneous vision-language tasks under a shared generative framework via instruction tuning, yet real-world deployment demands continuous capability expansion, making Multimodal Continual Instruction Tuning (MCIT) essential. Existing methods either update all tasks with a shared parameter set or allocate dedicated modules for each new task. Shared updates force heterogeneous tasks to compete, causing forgetting of learned capabilities. Conversely, isolated expansion prevents interference but severely limits parameter efficiency over long task streams. To address this dilemma, we propose CRAM. Specifically, by isolating task-specific patterns into independent modules, CRAM mitigates catastrophic forgetting across tasks. To further boost parameter efficiency, we utilize adaptive-rank instantiation to identify the capability gap between existing expert capability and new task demands, and dynamically allocate only the necessary parameters. To ensure stable reuse among tasks, centroid-guided routing recognizes and activates existing experts' capabilities, while an orthogonality penalty confines new updates to task-specific directions, preventing re-learning general capability. Extensive experiments across diverse benchmarks consistently demonstrate its superiority over existing methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes CRAM, a Centroid-Routing and Adaptive MoE framework for Multimodal Continual Instruction Tuning (MCIT). It isolates task-specific patterns into independent modules to mitigate catastrophic forgetting, employs adaptive-rank instantiation to quantify capability gaps between existing experts and new tasks for dynamic parameter allocation, uses centroid-guided routing for stable expert reuse, and applies an orthogonality penalty to confine updates to task-specific directions. The central claim is that this approach achieves superior performance and parameter efficiency over existing shared-update and isolated-module baselines across diverse benchmarks.

Significance. If the superiority and efficiency claims hold under rigorous verification, CRAM would represent a meaningful contribution to continual learning in MLLMs by addressing the shared-vs-isolated parameter trade-off through a combination of routing and adaptive allocation mechanisms.

major comments (3)
  1. [§3.2] §3.2 (Adaptive-Rank Instantiation): The procedure for quantifying the capability gap and selecting rank is described at a high level but lacks explicit specification of the metric, whether a validation pass or task-specific signals are used, and whether any post-selection adjustment occurs; this directly affects whether the reported parameter-efficiency gains are comparable to baselines that receive no equivalent tuning.
  2. [§4] §4 (Experiments): No error bars, standard deviations across runs, or details on baseline re-implementations and hyperparameter matching are provided, undermining the load-bearing claim that CRAM 'consistently demonstrates its superiority' over existing methods.
  3. [§3.3] §3.3 (Centroid-Guided Routing): The orthogonality penalty is introduced to prevent re-learning general capabilities, yet no analysis shows that the penalty term does not inadvertently constrain adaptation on tasks that legitimately require updates to shared directions; this is central to the forgetting-mitigation argument.
minor comments (2)
  1. [§3] Notation for expert centroids and routing scores is introduced without a consolidated table of symbols, making cross-section reference difficult.
  2. [Figure 2] Figure captions for routing visualizations do not state the number of tasks or experts visualized, reducing interpretability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, indicating where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Adaptive-Rank Instantiation): The procedure for quantifying the capability gap and selecting rank is described at a high level but lacks explicit specification of the metric, whether a validation pass or task-specific signals are used, and whether any post-selection adjustment occurs; this directly affects whether the reported parameter-efficiency gains are comparable to baselines that receive no equivalent tuning.

    Authors: We agree that the current description in §3.2 is insufficiently detailed. In the revised manuscript we will explicitly define the capability-gap metric (the performance delta on a small held-out set of task-specific examples), state that a single forward validation pass on new-task data is used without further tuning, and confirm that no post-selection adjustment is performed. These additions will allow direct comparison with baselines. revision: yes

  2. Referee: [§4] §4 (Experiments): No error bars, standard deviations across runs, or details on baseline re-implementations and hyperparameter matching are provided, undermining the load-bearing claim that CRAM 'consistently demonstrates its superiority' over existing methods.

    Authors: We acknowledge the omission. The revised §4 and a new appendix will report mean and standard deviation over at least three independent runs for every method and metric. We will also document the exact re-implementation protocol for each baseline, including the hyperparameter search ranges and selection criteria used to ensure fair matching. Where additional runs are feasible we will perform them; otherwise we will clearly note the original experimental settings. revision: yes

  3. Referee: [§3.3] §3.3 (Centroid-Guided Routing): The orthogonality penalty is introduced to prevent re-learning general capabilities, yet no analysis shows that the penalty term does not inadvertently constrain adaptation on tasks that legitimately require updates to shared directions; this is central to the forgetting-mitigation argument.

    Authors: The referee correctly identifies a missing analysis. While the centroid-routing mechanism is intended to permit reuse of existing experts for shared directions, we did not provide direct evidence that the orthogonality penalty leaves such reuse unimpeded. In the revision we will add an ablation that measures adaptation performance on tasks with controlled overlap to prior tasks, together with a visualization of gradient directions before and after the penalty, to substantiate the claim. revision: partial

Circularity Check

0 steps flagged

No significant circularity; method description contains no derivations or self-referential reductions

full rationale

The provided abstract and context contain no equations, parameter-fitting procedures, or derivation steps that could reduce to inputs by construction. Claims rest on high-level method descriptions and external experimental benchmarks rather than any self-definitional, fitted-input, or self-citation load-bearing chain. Absence of visible math or uniqueness theorems prevents identification of any circular step, consistent with the reader's note that no equations are present. This is the normal self-contained case for a methods paper without algebraic claims.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no explicit free parameters, axioms, or invented entities are stated. The method implicitly relies on the existence of identifiable task-specific patterns and measurable capability gaps, but these are not formalized.

pith-pipeline@v0.9.1-grok · 5726 in / 1085 out tokens · 23320 ms · 2026-06-28T14:15:36.956201+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

88 extracted references · 9 linked inside Pith

  1. [1]

    Advances in Neural Information Processing Systems , pages=

    Coin: A benchmark of continual instruction tuning for multimodel large language models , author=. Advances in Neural Information Processing Systems , pages=

  2. [2]

    ACL , pages=

    Hide-llava: Hierarchical decoupling for continual instruction tuning of multimodal large language model , author=. ACL , pages=

  3. [3]

    Jinpeng Chen and Runmin Cong and Yuzhi Zhao and Hongzheng Yang and Guangneng Hu and Horace Ip and Sam Kwong , booktitle=

  4. [4]

    Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages=

    Modalprompt: Towards efficient multimodal continual instruction tuning with dual-modality guided prompt , author=. Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages=

  5. [5]

    arXiv preprint arXiv:2411.02564 , year=

    Continual llava: Continual instruction tuning in large vision-language models , author=. arXiv preprint arXiv:2411.02564 , year=

    arXiv

  6. [6]

    arXiv preprint arXiv:2503.21227 , year=

    LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models , author=. arXiv preprint arXiv:2503.21227 , year=

    arXiv

  7. [7]

    ACL , pages=

    Progressive lora for multimodal continual instruction tuning , author=. ACL , pages=

  8. [8]

    Advances in Neural Information Processing Systems , volume=

    Learn to explain: Multimodal reasoning via thought chains for science question answering , author=. Advances in Neural Information Processing Systems , volume=

  9. [9]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Towards vqa models that can read , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  10. [10]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Imagenet: A large-scale hierarchical image database , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  11. [11]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Gqa: A new dataset for real-world visual reasoning and compositional question answering , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  12. [12]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Vizwiz grand challenge: Answering visual questions from blind people , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  13. [13]

    Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages=

    Referitgame: Referring to objects in photographs of natural scenes , author=. Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages=

  14. [14]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Making the v in vqa matter: Elevating the role of image understanding in visual question answering , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  15. [15]

    International Conference on Document Analysis and Recognition , pages=

    Ocr-vqa: Visual question answering by reading text in images , author=. International Conference on Document Analysis and Recognition , pages=. 2019 , organization=

    2019

  16. [16]

    International Conference on Machine Learning , pages=

    Learning transferable visual models from natural language supervision , author=. International Conference on Machine Learning , pages=

  17. [17]

    arXiv preprint arXiv:2510.08564 , year=

    How to Teach Large Multimodal Models New Skills , author=. arXiv preprint arXiv:2510.08564 , year=

    Pith/arXiv arXiv

  18. [18]

    Advances in Neural Information Processing Systems , pages =

    Visual Instruction Tuning , author=. Advances in Neural Information Processing Systems , pages =

  19. [19]

    Findings of the Association for Computational Linguistics: EMNLP , pages=

    Orthogonal subspace learning for language model continual learning , author=. Findings of the Association for Computational Linguistics: EMNLP , pages=

  20. [20]

    2025 , journal=

    Hierarchical Representation Matching for CLIP-based Class-Incremental Learning , author=. 2025 , journal=

    2025

  21. [22]

    arXiv preprint arXiv:2304.10592 , year=

    Minigpt-4: Enhancing vision-language understanding with advanced large language models , author=. arXiv preprint arXiv:2304.10592 , year=

    Pith/arXiv arXiv

  22. [23]

    Advances in Neural Information Processing Systems , volume=

    Instructblip: Towards general-purpose vision-language models with instruction tuning , author=. Advances in Neural Information Processing Systems , volume=

  23. [24]

    ACM Computing Surveys , year=

    Instruction tuning for large language models: A survey , author=. ACM Computing Surveys , year=

  24. [25]

    arXiv preprint arXiv:2310.02255 , year=

    Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts , author=. arXiv preprint arXiv:2310.02255 , year=

    Pith/arXiv arXiv

  25. [26]

    Proceedings of the IEEE international conference on computer vision , pages=

    Unit: Multimodal multitask learning with a unified transformer , author=. Proceedings of the IEEE international conference on computer vision , pages=

  26. [27]

    International Conference on Machine Learning , pages =

    Overcoming catastrophic forgetting with hard attention to the task , author =. International Conference on Machine Learning , pages =

  27. [28]

    ICLR , year =

    Effect of scale on catastrophic forgetting in neural networks , author =. ICLR , year =

  28. [29]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. ICLR , volume=

  29. [30]

    Advances in Neural Information Processing Systems , year =

    Nested Learning: The Illusion of Deep Learning Architectures , author =. Advances in Neural Information Processing Systems , year =

  30. [31]

    Proceedings of the IEEE/CVF 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 IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  31. [32]

    Neural computation , year=

    Adaptive mixtures of local experts , author=. Neural computation , year=

  32. [33]

    arXiv preprint arXiv:2410.10868 , year=

    Large Continual Instruction Assistant , author=. arXiv preprint arXiv:2410.10868 , year=

    arXiv

  33. [34]

    arXiv preprint arXiv:2505.22120 , year=

    LoKI: Low-damage Knowledge Implanting of Large Language Models , author=. arXiv preprint arXiv:2505.22120 , year=

    arXiv

  34. [35]

    Findings of the ACL: ACL , pages=

    Magic-vqa: Multimodal and grounded inference with commonsense knowledge for visual question answering , author=. Findings of the ACL: ACL , pages=

  35. [36]

    arXiv preprint arXiv:2512.23447 , year=

    Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss , author=. arXiv preprint arXiv:2512.23447 , year=

    arXiv

  36. [37]

    Proceedings of the IEEE international conference on computer vision , pages=

    SMoLoRA: Exploring and Defying Dual Catastrophic Forgetting in Continual Visual Instruction Tuning , author=. Proceedings of the IEEE international conference on computer vision , pages=

  37. [38]

    arXiv preprint arXiv:2302.13971 , year=

    Llama: Open and efficient foundation language models , author=. arXiv preprint arXiv:2302.13971 , year=

    Pith/arXiv arXiv

  38. [39]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Clevr: A diagnostic dataset for compositional language and elementary visual reasoning , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  39. [40]

    Advances in Neural Information Processing Systems , volume=

    A benchmark for compositional visual reasoning , author=. Advances in Neural Information Processing Systems , volume=

  40. [41]

    arXiv preprint arXiv:2311.07911 , year=

    Instruction-following evaluation for large language models , author=. arXiv preprint arXiv:2311.07911 , year=

    Pith/arXiv arXiv

  41. [42]

    arXiv preprint arXiv:2508.05580 , year=

    Follow-your-instruction: A comprehensive mllm agent for world data synthesis , author=. arXiv preprint arXiv:2508.05580 , year=

    arXiv

  42. [43]

    Advances in Neural Information Processing Systems , volume =

    Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima , author =. Advances in Neural Information Processing Systems , volume =

  43. [44]

    International Conference on Machine Learning , pages=

    The flan collection: Designing data and methods for effective instruction tuning , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  44. [45]

    arXiv preprint arXiv:2511.15164 , year=

    Multimodal Continual Instruction Tuning with Dynamic Gradient Guidance , author=. arXiv preprint arXiv:2511.15164 , year=

    arXiv

  45. [46]

    arXiv preprint arXiv:2506.02011 , year=

    OASIS: Online Sample Selection for Continual Visual Instruction Tuning , author=. arXiv preprint arXiv:2506.02011 , year=

    arXiv

  46. [47]

    arXiv preprint arXiv:2508.04227 , year=

    Continual learning for VLMs: A survey and taxonomy beyond forgetting , author=. arXiv preprint arXiv:2508.04227 , year=

    Pith/arXiv arXiv

  47. [48]

    arXiv preprint arXiv:2506.08666 , year=

    LLaVA-c: Continual Improved Visual Instruction Tuning , author=. arXiv preprint arXiv:2506.08666 , year=

    arXiv

  48. [49]

    Science , volume =

    Adityanarayanan Radhakrishnan and Daniel Beaglehole and Parthe Pandit and Mikhail Belkin , title =. Science , volume =

  49. [50]

    Proceedings of the IEEE international conference on computer vision , pages=

    Metamorph: Multimodal understanding and generation via instruction tuning , author=. Proceedings of the IEEE international conference on computer vision , pages=

  50. [51]

    ACL , pages=

    Mammoth-vl: Eliciting multimodal reasoning with instruction tuning at scale , author=. ACL , pages=

  51. [52]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

    Otter: A multi-modal model with in-context instruction tuning , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

  52. [53]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    Thinking in space: How multimodal large language models see, remember, and recall spaces , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  53. [54]

    Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages=

    Multimodal arxiv: A dataset for improving scientific comprehension of large vision-language models , author=. Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages=

  54. [55]

    arXiv preprint arXiv:2208.05358 , year =

    Clevr-math: A dataset for compositional language, visual and mathematical reasoning , author =. arXiv preprint arXiv:2208.05358 , year =

    arXiv

  55. [56]

    arXiv preprint arXiv:2110.13214 , year=

    Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning , author=. arXiv preprint arXiv:2110.13214 , year=

    arXiv

  56. [57]

    Proceedings of the IEEE international conference on computer vision , pages =

    The many faces of robustness: A critical analysis of out-of-distribution generalization , author =. Proceedings of the IEEE international conference on computer vision , pages =

  57. [58]

    Proceedings of the IEEE international conference on computer vision , pages=

    Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models , author=. Proceedings of the IEEE international conference on computer vision , pages=

  58. [59]

    2023 , journal=

    PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering , author=. 2023 , journal=

    2023

  59. [60]

    Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

    Docvqa: A dataset for vqa on document images , author=. Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=

  60. [61]

    Findings of the association for computational linguistics , pages=

    Chartqa: A benchmark for question answering about charts with visual and logical reasoning , author=. Findings of the association for computational linguistics , pages=

  61. [62]

    Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

    Infographicvqa , author=. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

  62. [63]

    Proceedings of the AAAI Conference on Artificial Intelligence , pages=

    RoadSceneVQA: Benchmarking Visual Question Answering in Roadside Perception Systems for Intelligent Transportation System , author=. Proceedings of the AAAI Conference on Artificial Intelligence , pages=

  63. [64]

    IEEE Transactions on Visualization and Computer Graphics , volume=

    ChemVA: interactive visual analysis of chemical compound similarity in virtual screening , author=. IEEE Transactions on Visualization and Computer Graphics , volume=

  64. [65]

    IEEE Access , volume=

    Floodnet: A high resolution aerial imagery dataset for post flood scene understanding , author=. IEEE Access , volume=

  65. [66]

    arXiv preprint arXiv:2602.01990 , year=

    SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning , author=. arXiv preprint arXiv:2602.01990 , year=

    Pith/arXiv arXiv

  66. [67]

    Proceedings of the IEEE international conference on computer vision , pages=

    Federated continual instruction tuning , author=. Proceedings of the IEEE international conference on computer vision , pages=

  67. [68]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Ask and remember: A questions-only replay strategy for continual visual question answering , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  68. [69]

    arXiv preprint arXiv:2408.14471 , year=

    A Practitioner's Guide to Continual Multimodal Pretraining , author=. arXiv preprint arXiv:2408.14471 , year=

    arXiv

  69. [70]

    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=

  70. [71]

    arXiv preprint arXiv:2503.01887 , year=

    When continue learning meets multimodal large language model: A survey , author=. arXiv preprint arXiv:2503.01887 , year=

    arXiv

  71. [72]

    Proceedings of the AAAI Conference on Artificial Intelligence , pages=

    LoRA in LoRA: Towards parameter-efficient architecture expansion for continual visual instruction tuning , author=. Proceedings of the AAAI Conference on Artificial Intelligence , pages=

  72. [73]

    arXiv preprint arXiv:2506.11672 , year=

    Dynamic mixture of curriculum lora experts for continual multimodal instruction tuning , author=. arXiv preprint arXiv:2506.11672 , year=

    arXiv

  73. [74]

    IJCAI , pages=

    Continual learning with pre-trained models: a survey , author=. IJCAI , pages=

  74. [75]

    Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

    icarl: Incremental classifier and representation learning , author=. Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition Conference , pages=

  75. [76]

    PNAS , volume=

    Overcoming catastrophic forgetting in neural networks , author=. PNAS , volume=

  76. [77]

    Proceedings of the Workshop on Towards Knowledgeable Foundation Models (KnowFM) , pages=

    MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models , author=. Proceedings of the Workshop on Towards Knowledgeable Foundation Models (KnowFM) , pages=

  77. [78]

    arXiv preprint arXiv:2508.07307 , year=

    Mcitlib: Multimodal continual instruction tuning library and benchmark , author=. arXiv preprint arXiv:2508.07307 , year=

    arXiv

  78. [79]

    International Conference on Learning Representations , year=

    Quantized Gradient Projection for Memory-Efficient Continual Learning , author=. International Conference on Learning Representations , year=

  79. [80]

    arXiv preprint arXiv:2506.05453 , year=

    Mllm-cl: Continual learning for multimodal large language models , author=. arXiv preprint arXiv:2506.05453 , year=

    arXiv

  80. [81]

    International Conference on Learning Representations , year=

    When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations , author=. International Conference on Learning Representations , year=

Showing first 80 references.