Adaptation Method for Misinformation Identification

Hao Chen; Jiajie Xu; Jia Zhu; Mengze Li; Weijie Shi; Yangping Chen; Yue Cui

REVIEW 6 major objections 6 minor 64 references

Adaptation Method for Misinformation Identification

T0 review · 6 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read ADOSE is an active domain adaptation framework for multimodal fake news detection; the paper claims it outperforms existing methods by 2.72% to 14.02% average accuracy.

desk verdict A sensible incremental combination of active learning and domain adaptation for multimodal fake news, but the headline gains are inflated by a weak baseline and the evaluation lacks variance; worth a real review, not a desk reject. read the letter →

arxiv 2504.14171 v1 pith:KIDK5XMX submitted 2025-04-19 cs.AI

Yangping Chen , Weijie Shi , Mengze Li , Yue Cui , Hao Chen , Jia Zhu , Jiajie Xu This is my paper · ORCID

classification cs.AI

keywords activedomainadaptationmultimodalfakenewsdetectionleast-disagreeuncertaintydiversity-basedsamplingcross-modaldependencylearningGaussianperturbationshifttext-imagemismatch

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

ADOSE is an active domain adaptation framework for multimodal fake news detection. The paper claims that by training three expert classifiers—one on text, one on images, and one on cross-modal pairs—and choosing which target-domain samples to annotate through a least-disagree uncertainty score plus a multi-view diversity score, a model can adapt to a new news topic with only 10% of target labels. On two standard datasets the paper reports average accuracy gains of 3.24%–14.02% (Pheme) and 2.72%–7.04% (Weibo) over existing active and unsupervised domain adaptation baselines. If correct, this gives practitioners a working recipe for cross-topic fake news detection without waiting for large amounts of newly labeled data.

What carries the argument

The load-bearing object is the least-disagree metric (LDM): the smallest probability, over hypotheses that flip a sample's predicted label, that two hypotheses disagree on a random target sample. Because the true LDM cannot be computed, the paper replaces it with an estimator that perturbs the last-layer weights of each expert classifier with Gaussian noise across several variance scales and counts how often the fused prediction flips; smaller estimated values mark samples close to the decision boundary. Around this estimator the framework builds a three-classifier fusion network (text, image, and cross-modal experts) and a diversity scorer that averages cosine similarities over the three modality views to pick the most representative subset.

What would settle it

Run the ADOSE pipeline on Pheme and Weibo with the LUS selection step replaced by random selection of the same number of target samples, keeping MEFN and MDC fixed. If average accuracy stays within the reported 3.24–14.02% range over baseline methods, the least-disagree uncertainty estimate is not what drives the gains.

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Extended reading notes

Core claim

The paper's central claim is that domain shift in multimodal fake news detection can be handled by separating three kinds of deception evidence—within-text errors, within-image errors, and text–image inconsistencies—and actively annotating the target samples that sit closest to the current decision boundary while also covering the target feature space. The proposed pipeline, ADOSE, fuses the predictions of two unimodal classifiers and one cross-modal classifier (MEFN), ranks unlabeled target samples by an estimated least-disagree metric obtained from Gaussian weight perturbations (LUS), then re-ranks the top candidates by diversity across text, image, and cross-modal feature views (MDC). The experiments on Pheme and Weibo report average accuracy of 83.23% and 86.66%, respectively, exceeding all compared unsupervised and active domain adaptation methods on the average metrics.

Load-bearing premise

The method's gains rest on the assumption that adding Gaussian noise to the final classifier weights creates a family of plausible models whose smallest disagreement rate measures a sample's true proximity to the decision boundary, so that the samples most affected by such noise are the most informative ones to label.

Editorial extensions

If this is right

A fixed small annotation budget—10% of the target domain—can be enough to transfer a fake news detector from one event or topic domain to another.
Decomposing deception patterns into intra-modal and inter-modal dependencies and scoring them separately gives a measurable edge over a single fused classifier.
Uncertainty-based selection alone leaves redundant samples in the annotated set; adding a multi-view diversity filter improves average accuracy, especially when source and target topics differ sharply.
The LUS ranking can identify target samples whose deception patterns deviate most from the source domain, which is exactly the subset that helps the model adapt.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

One testable extension is to calibrate the perturbation schedule (number of rounds K, samples per round J, variance range) on a held-out domain pair; the paper does not report these values, so its headline gains may be sensitive to them.
The same uncertainty-plus-diversity selection could be applied to other multimodal verification tasks such as rumor detection in social media posts or AI-generated image–text pairs, since the selection logic does not depend on news-specific features.
If domain divergence between source and target is small, the ablation suggests uncertainty selection matters more than diversity; a production system could tune the m multiplier per domain pair rather than per dataset.
The paper's comparison against CLUE's low Pheme F1(fake) score hints that clustering-based ADA methods may be poorly suited to binary fake news data, though the paper does not test this directly.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things about this paper. First, the core idea is reasonable: it combines three expert classifiers (text, image, cross-modal) with active selection that mixes least-disagree uncertainty and multi-view diversity, and it targets a real operational problem—reducing annotation cost for cross-domain multimodal fake news detection. Second, the headline performance claims are not yet established. The evaluation has no error bars or significance tests, and most baselines do not share the paper's MEFN backbone, so the reported 2.72–14.02% margins likely conflate architecture with selection strategy. The one controlled comparison, ADOSE versus Entropy (which does use the MEFN backbone), gives margins of only about 3% on each dataset—plausible but not confirmed without repeated runs.

The paper does some things well. The ablation study is genuinely informative: removing LUS hurts most, then MEFN, then MDC, which is consistent with the design logic. The comparison of ADOSE to its own no-active-selection variant (ADOSE-UDA) shows that active selection adds value. The problem formulation is clear, and the method is described at a conceptual level well enough for someone to reimplement the main pieces. The authors also cite prior work on the least-disagree metric explicitly, which is honest.

The soft spots are in proportion to the claims. The biggest issue is statistical: for deep learning experiments, 2–4% differences can easily be noise, and without multiple seeds we cannot tell. The headline range is misleading because the upper bound (14.02%) comes almost entirely from CLUE's anomalously low performance on Pheme (Avg F1(fake)=20.16), which suggests an implementation mismatch. A more honest claim would focus on the ~3% margin over the same-backbone Entropy baseline. The LUS module, which the ablation says is the most important, is missing key hyperparameters: the paper never reports K (perturbation rounds), J (weight samples), or the variance schedule. The method is not fully reproducible as written. Finally, the multiplier m is tuned per dataset (2 for Pheme, 5 for Weibo) with no validation protocol described, which raises a mild overfitting concern.

None of these flaws are fatal—the method is not circular, the writing is honest, and the ablations support the internal logic. This paper is for people working on fake news detection or active domain adaptation specifically. A reader in that area gets a useful baseline and a concrete example of how evaluation pitfalls can inflate performance claims. I would send it to peer review rather than desk-reject it, but I would ask the authors to release code, report variance across seeds, run backbone-controlled baselines, and specify the LUS details before publishing any specific margin as fact.

Referee Report

6 major / 6 minor

Summary. The paper proposes ADOSE, an Active Domain Adaptation (ADA) framework for multimodal fake news detection, combining a Modal-dependency Expertise Fusion Network (MEFN) with three expert classifiers (text, image, cross-modal), a Least-disagree Uncertainty Selector (LUS), and a Multi-view Diversity Calculator (MDC). The MEFN uses adversarial training and contrastive learning to obtain domain-invariant and semantically aligned unimodal and cross-modal features; LUS selects uncertain samples by approximating a least-disagree metric via Gaussian weight perturbations; MDC then diversifies the candidate set using multi-view similarity. Experiments on Pheme and Weibo report that ADOSE outperforms existing ADA methods by 2.72%-14.02% in average accuracy, with ablations showing each module contributes to the final result.

Significance. If the claimed improvements are reliable, this would be a meaningful step for active domain adaptation in multimodal fake news detection, where fine-grained modality dependencies and target-domain informativeness both matter. The paper also demonstrates a sensible decomposition into intra-modal and inter-modal expertise and an active selection criterion tailored to domain shift. However, the current empirical evidence is weakened by backbone mismatches in the baseline comparison, absent statistical significance reporting, and per-dataset tuning of a key hyperparameter; the contribution is therefore promising but not yet fully established.

major comments (6)

[Section 5.1.4, Table 1] The central claim of outperforming existing ADA methods is confounded by backbone mismatch. Only the Entropy baseline is explicitly run on the MEFN backbone; Detective, CLUE, and EADA are used as their original methods. Since the ablation in Table 2 shows MEFN itself contributes 4.00 accuracy points on Pheme (83.23 vs 79.23 without MEFN) and 3.01 points on Weibo (86.66 vs 83.65), the large margins over those baselines cannot be attributed solely to the LUS+MDC selection strategy. The backbone-matched comparison against Entropy yields much smaller margins (3.24% on Pheme, 2.72% on Weibo). Please rerun all ADA baselines on the same MEFN backbone, or provide an explicit decomposition of the contribution of the backbone versus the selection strategy.
[Tables 1 and 2] No variance or statistical significance measures are reported. Tables 1 and 2 list single-point estimates without standard deviations, number of seeds, or significance tests. Given that the smallest backbone-controlled margin is only about 2.7-3.2 percentage points, the reported improvements cannot be judged as reliable without such statistics. In addition, the CLUE result on Pheme (Avg. Acc=69.21, Avg. F1(fake)=20.16) is an extreme outlier relative to other methods and suggests an unstable or failed run; the paper should examine and explain this anomaly.
[Section 5.1.2, Figure 3] The multiplier m in the selection process is tuned per dataset: the paper states m=2 for Pheme and m=5 for Weibo. Figure 3 shows that accuracy is sensitive to m, and the tuning is done on the same datasets used for the final evaluation without a held-out validation split. This constitutes a form of test-set selection and inflates the reported gains. Please either fix m a priori, use a validation-based selection procedure, or discuss the risk of per-dataset overfitting.
[Section 4.2] The LUS component, which the ablation identifies as the most impactful, does not report its core hyperparameters. The number of perturbation rounds K, the number of weight samplings J, and the variance schedule {sigma_k^2} are defined in Equations (11)-(15) but never specified in the experimental setup. This prevents reproduction and leaves open the question of how sensitive the method is to these choices. Provide the concrete values and include an ablation over K and J to demonstrate stability of the LDM approximation for the multimodal fusion network.
[Equation (8)] Equation (8) as printed is not a valid probability normalization: P_mefn = softmax(score_y / log_{y' in Y} exp(score_y')) divides the logits by the log-sum-exp of the same logits, which is not the standard product-of-experts softmax. I suspect the intended formula is P_mefn = softmax(score_y), with score_y defined as the sum of log-probabilities from the three classifiers. Please correct the equation and verify that the implemented model matches the corrected formula.
[Section 5.3, Table 2] The ablation setup for 'w/o LUS' is underspecified. Removing the Least-disagree Uncertainty Selector could mean random target sample selection, diversity-only selection, or another replacement strategy. Since the paper argues that LUS is the core selection mechanism, it is essential to state exactly what selection strategy replaces it in this ablation so the reader can interpret the large drop in accuracy (77.67 on Pheme, 80.45 on Weibo).

minor comments (6)

[Section 4.1.1] In the definition of the adversarial loss, the sentence reads 'L_adv = L_abv_t + L_abv_t'; the second term should be L_abv_v.
[Equation (12)] There is a typo in the perturbation notation: 'eW_cla_v' should be 'eW_cls_v' for consistency with the other classifier weight symbols.
[Section 4.4] The word 'hypermeter' should be 'hyperparameter', and in Section 6 'experrt' should be 'expert'.
[Equation (7)] The lower limit of the summation over negative samples is typeset incorrectly ('b˝ i≠j'); please clarify the index range for the contrastive loss.
[Section 5.1.1] The total number of target unlabeled samples N_tu for each dataset is not reported; providing these numbers would help readers interpret the active annotation budget k = B/5.
[Section 5.4] The case study uses subjective visual descriptions such as 'brighter' and 'darker' without quantitative evidence; consider showing the corresponding feature distances or model confidence values to make the illustrations more rigorous.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ADOSE's central claim is an empirical benchmark comparison, and its active-selection mechanism is adapted from external prior work rather than derived from the target result.

full rationale

The paper's derivation chain contains no step where a prediction is equivalent to its inputs by construction. The Modal-dependency Expertise Fusion Network (MEFN) is composed of standard losses—cross-entropy, adversarial domain discrimination (Eqs. 3–4), contrastive alignment (Eq. 7), and a JS-divergence negotiation term (Eq. 20)—and the final objective (Eq. 21) is a weighted sum of these defined losses; none of these equations presupposes Table 1's accuracy numbers. The Least-disagree Uncertainty Selector imports the LDM approximation from Cho et al. [2], an external ICLR paper, explicitly: 'We follow the approximation method for LDM proposed in the work [2] to estimate the LDM value for each target domain sample.' That is a borrowed method, not a self-citation chain, and the paper does not claim to derive the approximation anew. The Multi-view Diversity Calculator is defined self-containedly by Eq. (17) from the classifier features, and while there is an internal inconsistency in the text (the formula computes average cosine similarity but the text calls larger values 'more dissimilar,' which would invert the intended selection), that is a correctness/implementation bug, not a circularity. The comparison claims are measured outcomes on Pheme and Weibo, and 'ADOSE-UDA' is explicitly described as ADOSE with active selection removed, used as an ablation-style baseline rather than as an independent predictor. Self-citations exist in the references (e.g., [55] Detective), but they are used as baselines or related work and are not load-bearing for ADOSE's own derivation. Two genuine experimental concerns—the lack of variance reporting and the fact that most ADA baselines do not share the MEFN backbone—are threats to the soundness or generality of the empirical claim, but they are not circular reasoning: the baseline numbers are externally measured comparisons, not outputs derived from ADOSE's own assumptions. No step in the paper reduces an Eq. (X) to an Eq. (Y) by definition, and no fitted parameter is renamed as a prediction. The appropriate finding is therefore no significant circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper is empirical; its central claim rests on standard but unproven active-learning and domain-adaptation assumptions. The LDM approximation is carried over from prior work without validation on this architecture, and key hyperparameters are unreported. No new physical or conceptual entities are introduced; the free parameters are the loss weights, the candidate multiplier m, and the unspecified LUS and contrastive hyperparameters.

free parameters (5)

m (candidate multiplier in LUS) = 2 on Pheme, 5 on Weibo
Controls how many uncertain candidates proceed to the diversity stage; selected per dataset by experimental search (Section 5.1.2, Figure 3).
Loss weights lambda_c, lambda_t, lambda_a, lambda_n = 0.5, 0.5, 0.2, 0.2
Determined through experimental search in Section 5.1.2; balance classification, adversarial, contrastive, and negotiation losses.
Perturbation rounds K and weight samples J = not reported
Used in LUS (Section 4.2) to define the hypothesis set; concrete values are omitted, yet the uncertainty estimate depends on them.
Variance schedule sigma_k = not reported
The set of linearly increasing variances for Gaussian perturbations (Section 4.2); no specific values are provided.
Contrastive threshold beta and temperature tau = not reported
Used in Eq. (6)-(7) for negative sample filtering and contrastive loss; values are never specified.

assumptions (5)

domain assumption Gaussian perturbations of the last-layer classifier weights produce a hypothesis set whose smallest disagreement rate approximates the true least-disagree metric.
Adopted from prior work [2] in Section 4.2, but not validated for the multimodal fusion network; K, J, and sigma_k are unspecified.
domain assumption Samples closest to the decision boundary are the most informative for domain adaptation.
Standard active learning heuristic invoked in Section 4.2; no empirical validation specific to multimodal fake news.
domain assumption Cross-modal correspondence is more likely to exist in real news than in misinformation.
Stated in Section 4.1.2 and used to construct the contrastive loss; fake news can also have consistent text-image pairs, e.g., fabricated images with matching text.
domain assumption The three expert classifiers should agree in their predictions on real news.
The negotiation loss in Eq. (20) assumes this agreement is beneficial; it is a regularization prior, not an established property.
domain assumption Adversarial domain-invariant features transfer across news domains.
Standard domain adaptation assumption in Section 4.1.1; the case study itself suggests that domain-specific cues (e.g., image color style) may be useful, creating tension.

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Pith. "Pith review of Adaptation Method for Misinformation Identification." pith.science (2026). https://pith.science/paper/KIDK5XMX

@misc{pith2026250414171,
  author       = {Pith},
  title        = {Pith review of: Adaptation Method for Misinformation Identification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KIDK5XMX}},
  note         = {Machine review of arXiv:2504.14171}
}

abstract

Multimodal fake news detection plays a crucial role in combating online misinformation. Unfortunately, effective detection methods rely on annotated labels and encounter significant performance degradation when domain shifts exist between training (source) and test (target) data. To address the problems, we propose ADOSE, an Active Domain Adaptation (ADA) framework for multimodal fake news detection which actively annotates a small subset of target samples to improve detection performance. To identify various deceptive patterns in cross-domain settings, we design multiple expert classifiers to learn dependencies across different modalities. These classifiers specifically target the distinct deception patterns exhibited in fake news, where two unimodal classifiers capture knowledge errors within individual modalities while one cross-modal classifier identifies semantic inconsistencies between text and images. To reduce annotation costs from the target domain, we propose a least-disagree uncertainty selector with a diversity calculator for selecting the most informative samples. The selector leverages prediction disagreement before and after perturbations by multiple classifiers as an indicator of uncertain samples, whose deceptive patterns deviate most from source domains. It further incorporates diversity scores derived from multi-view features to ensure the chosen samples achieve maximal coverage of target domain features. The extensive experiments on multiple datasets show that ADOSE outperforms existing ADA methods by 2.72\% $\sim$ 14.02\%, indicating the superiority of our model.

Figures

Figures reproduced from arXiv: 2504.14171 by the authors.

**Figure 2.** The network architecture of ADOSE. (a) Modal-dependency Expertise Fusion Network (MEFN) utilizes multiple classifiers to [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Accuracy trends with varying 𝑚 (the x-axis represents the different values of 𝑚 as 2, 3, 4, 5, 10). to Weibo (whose domains differ significantly in topic), making uncertainty estimation more crucial than diversity measurement [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: The case study is conducted using target domain sam [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 58 canonical work pages

[1]

Yahui Chen. 2015. Convolutional neural network for sentence classification . Master’s thesis. University of Waterloo

work page 2015
[2]

Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, and Chang D Yoo

work page
[3]

Chaoqun Cui and Caiyan Jia. 2025. Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning. In Proceedings of the 31st International Conference on Computational Linguistics

work page 2025
[4]

Zhenlong Dai, Bingrui Chen, Zhuoluo Zhao, Xiu Tang, Sai Wu, Chang Yao, Zhipeng Gao, and Jingyuan Chen. 2025. Less is More: Adaptive Program Repair with Bug Localization and Preference Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, V ol. 39. 128–136

work page 2025
[5]

Zhenlong Dai, Chang Yao, WenKang Han, Yuanying Yuanying, Zhipeng Gao, and Jingyuan Chen. 2024. MPCoder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3765–3780

2024
[6]

Yiqi Dong, Dongxiao He, Xiaobao Wang, Youzhu Jin, Meng Ge, Carl Yang, and Di Jin. 2024. Unveiling implicit deceptive patterns in multi-modal fake news via Adaptation Method for Misinformation Identification Conference acronym ’XX, October 27–31, 2025, Dublin, Ireland neuro-symbolic reasoning. In Proceedings of the AAAI Conference on Artificial Intelligenc...

work page 2024
[7]

Zhekai Du and Jingjing Li. 2023. Diffusion-based probabilistic uncertainty esti- mation for active domain adaptation. Advances in Neural Information Processing Systems 36 (2023), 17129–17155

work page 2023
[8]

Fujun Han, Peng Ye, Shukai Duan, and Lidan Wang. 2024. Ada-iD: Active Domain Adaptation for Intrusion Detection. In Proceedings of the 32nd ACM International Conference on Multimedia. 7404–7413

work page 2024

Show all 64 references

[9]

Steve Hanneke et al. 2014. Theory of disagreement-based active learning. Foun- dations and Trends® in Machine Learning (2014)

2014
[10]

Wei Ji, Renjie Liang, Zhedong Zheng, Wenqiao Zhang, Shengyu Zhang, Juncheng Li, Mengze Li, and Tat-seng Chua. 2023. Are binary annotations sufficient? video moment retrieval via hierarchical uncertainty-based active learning. InProceedings of the IEEE/CVF conference on compute...

2023
[11]

Yoon-Yeong Kim, Youngjae Cho, JoonHo Jang, Byeonghu Na, Yeongmin Kim, Kyungwoo Song, Wanmo Kang, and Il-Chul Moon. 2023. Saal: sharpness-aware active learning. In International Conference on Machine Learning. PMLR

2023
[12]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic opti- mization. arXiv preprint arXiv:1412.6980 (2014)

2014 arXiv
[13]

Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, and Siliang Tang. 2022. Fine-grained semantically aligned vision-language pre-training. Advances in neural information processing systems (2022)

2022
[14]

Juncheng Li, Kaihang Pan, Zhiqi Ge, Minghe Gao, Wei Ji, Wenqiao Zhang, Tat- Seng Chua, Siliang Tang, Hanwang Zhang, and Yueting Zhuang. 2023. Fine-tuning multimodal llms to follow zero-shot demonstrative instructions. In The Twelfth International Conference on Learning Representations

2023
[15]

Juncheng Li, Siliang Tang, Linchao Zhu, Wenqiao Zhang, Yi Yang, Tat-Seng Chua, and Fei Wu. 2023. Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)

2023
[16]

Juncheng Li, Xin Wang, Siliang Tang, Haizhou Shi, Fei Wu, Yueting Zhuang, and William Yang Wang. 2020. Unsupervised reinforcement learning of transferable meta-skills for embodied navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

2020
[17]

Juncheng Li, Junlin Xie, Linchao Zhu, Long Qian, Siliang Tang, Wenqiao Zhang, Haochen Shi, Shengyu Zhang, Longhui Wei, Qi Tian, et al. 2022. Dilated context integrated network with cross-modal consensus for temporal emotion localiza- tion in videos. In Proceedings of the 30th ...

2022
[18]

Li Li, Wei Ji, Yiming Wu, Mengze Li, You Qin, Lina Wei, and Roger Zimmermann
[19]

Mengze Li, Han Wang, Wenqiao Zhang, Jiaxu Miao, Zhou Zhao, Shengyu Zhang, Wei Ji, and Fei Wu. 2023. Winner: Weakly-supervised hierarchical decomposi- tion and alignment for spatio-temporal video grounding. In Proceedings of the IEEE/CVF conference on computer vision and patter...

2023
[20]

Mengze Li, Haoyu Zhang, Juncheng Li, Zhou Zhao, Wenqiao Zhang, Shengyu Zhang, Shiliang Pu, Yueting Zhuang, and Fei Wu. 2023. Unsupervised domain adaptation for video object grounding with cascaded debiasing learning. In Pro- ceedings of the 31st ACM International Conference on...

2023
[21]

Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, et al. 2023. ART: rule bAsed futuRe-inference deducTion. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

2023
[22]

Yichuan Li, Kyumin Lee, Nima Kordzadeh, Brenton Faber, Cameron Fiddes, Elaine Chen, and Kai Shu. 2021. Multi-source domain adaptation with weak supervision for early fake news detection. In 2021 IEEE International Conference on Big Data (Big Data). IEEE

2021
[23]

Tianwei Lin, Wenqiao Zhang, Sijing Li, Yuqian Yuan, Binhe Yu, Haoyuan Li, Wanggui He, Hao Jiang, Mengze Li, Xiaohui Song, et al. 2025. HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Genera- tion via Heterogeneous Knowledge Adaptation. arXiv pre...

2025 arXiv
[24]

Wang Lin, Jingyuan Chen, Jiaxin Shi, Zirun Guo, Yichen Zhu, Zehan Wang, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Y AN, et al. 2024. Action imitation in common action space for customized action image synthesis. In The Thirty-eighth Annual Conference on Neural Information Processi...

2024
[25]

Wang Lin, Jingyuan Chen, Jiaxin Shi, Yichen Zhu, Chen Liang, Junzhong Miao, Tao Jin, Zhou Zhao, Fei Wu, Shuicheng Yan, et al. 2024. Non-confusing generation of customized concepts in diffusion models. arXiv preprint arXiv:2405.06914 (2024)

2024 arXiv
[26]

Guofan Liu, Jinghao Zhang, Qiang Liu, Junfei Wu, Shu Wu, and Liang Wang
[27]

Hui Liu, Wenya Wang, Hao Sun, Anderson Rocha, and Haoliang Li. 2023. Ro- bust domain misinformation detection via multi-modal feature alignment. IEEE Transactions on Information Forensics and Security19 (2023), 793–806

2023
[28]

Shizhan Liu, Zhengkai Jiang, Yuxi Li, Jinlong Peng, Yabiao Wang, and Weiyao Lin
[29]

IEEE Transactions on Knowledge and Data Engineering(2024)

Uni-Modal Event-Agnostic Knowledge Distillation for Multimodal Fake News Detection. IEEE Transactions on Knowledge and Data Engineering(2024)

2024
[30]

Han Lu, Yichen Xie, Xiaokang Yang, and Junchi Yan. 2024. Boundary Matters: A Bi-Level Active Finetuning Method. Advances in Neural Information Processing Systems (2024)

2024
[31]

Yuwu Lu, Haoyu Huang, and Xue Hu. 2024. Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation. Advances in Neural Information Processing Systems (2024)

2024
[32]

In Proceedings of the AAAI Conference on Artificial Intelligence

Density matters: improved core-set for active domain adaptive segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence
[33]

Ye Liu, Lingfeng Qiao, Changchong Lu, Di Yin, Chen Lin, Haoyuan Peng, and Bo Ren. 2023. OSAN: A one-stage alignment network to unify multimodal alignment and unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

2023
[34]

Zihan Ma, Minnan Luo, Hao Guo, Zhi Zeng, Yiran Hao, and Xiang Zhao. 2024. Event-radar: Event-driven multi-view learning for multimodal fake news detection. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 5809–5821

2024
[35]

Divyam Madaan, Taro Makino, Sumit Chopra, and Kyunghyun Cho. 2024. Jointly Modeling Inter-& Intra-Modality Dependencies for Multi-modal Learning. Ad- vances in Neural Information Processing Systems (2024)

2024
[36]

Xiangwei Lv, Jingyuan Chen, Mengze Li, Yongduo Sui, Zemin Liu, and Beishui Liao. [n. d.]. Grasp the Key Takeaways from Source Domain for Few Shot Graph Domain Adaptation. In THE WEB CONFERENCE 2025

2025
[37]

Xinhong Ma, Junyu Gao, and Changsheng Xu. 2021. Active universal domain adaptation. In Proceedings of the IEEE/CVF International Conference on Com- puter Vision

2021
[38]

Jiaxu Miao, Yu Wu, Ping Liu, Yuhang Ding, and Yi Yang. 2019. Pose-guided feature alignment for occluded person re-identification. In Proceedings of the IEEE/CVF international conference on computer vision

2019
[39]

Yuzuru Nakamura, Yasunori Ishii, and Takayoshi Yamashita. 2024. Active Domain Adaptation with False Negative Prediction for Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

2024
[40]

Jiaxu Miao, Yunchao Wei, Xiaohan Wang, and Yi Yang. 2023. Temporal pixel- level semantic understanding through the vspw dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)

2023
[41]

Jiaxu Miao, Yunchao Wei, Yu Wu, Chen Liang, Guangrui Li, and Yi Yang. 2021. Vspw: A large-scale dataset for video scene parsing in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

2021
[42]

Yu Tong, Weihai Lu, Zhe Zhao, Song Lai, and Tong Shi. 2024. MMDFND: Multi-modal Multi-Domain Fake News Detection. In Proceedings of the 32nd ACM International Conference on Multimedia. 1178–1186

2024
[43]

Dan Wang and Yi Shang. 2014. A new active labeling method for deep learning. In 2014 International joint conference on neural networks (IJCNN). IEEE

2014
[44]

Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman. 2021. Active domain adaptation via clustering uncertainty-weighted embeddings. In Proceedings of the IEEE/CVF international conference on computer vision. 8505– 8514

2021
[45]

Hongyan Ran and Caiyan Jia. 2023. Unsupervised cross-domain rumor detec- tion with contrastive learning and cross-attention. In Proceedings of the AAAI Conference on Artificial Intelligence

2023
[46]

Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. Eann: Event adversarial neural networks for multi- modal fake news detection. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining

2018
[47]

Yaqing Wang, Fenglong Ma, Haoyu Wang, Kishlay Jha, and Jing Gao. 2021. Multimodal emergent fake news detection via meta neural process networks. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 3708–3716

2021
[48]

Jiandong Wang, Hongguang Zhang, Chun Liu, and Xiongjun Yang. 2024. Fake news detection via multi-scale semantic alignment and cross-modal attention. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2406–2410

2024
[49]

Longzheng Wang, Chuang Zhang, Hongbo Xu, Yongxiu Xu, Xiaohan Xu, and Siqi Wang. 2023. Cross-modal contrastive learning for multimodal fake news detection. In Proceedings of the 31st ACM international conference on multimedia. 5696–5704

2023
[50]

Mixue Xie, Shuang Li, Rui Zhang, and Chi Harold Liu. 2023. Dirichlet- based uncertainty calibration for active domain adaptation. arXiv preprint arXiv:2302.13824 (2023). Conference acronym ’XX, October 27–31, 2025, Dublin, Ireland Y anping Chen, Weijie Shi, Mengze Li, Yue Cui,...

2023 arXiv
[51]

Huaiwen Zhang, Shengsheng Qian, Quan Fang, and Changsheng Xu. 2020. Mul- timodal disentangled domain adaption for social media event rumor detection. IEEE Transactions on Multimedia (2020)

2020
[52]

Tao Wu, Mengze Li, Jingyuan Chen, Wei Ji, Wang Lin, Jinyang Gao, Kun Kuang, Zhou Zhao, and Fei Wu. 2024. Semantic Alignment for Multimodal Large Language Models. In Proceedings of the 32nd ACM International Conference on Multimedia

2024
[53]

Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, and Guoren Wang. 2022. Active learning for domain adaptation: An energy-based approach. In Proceedings of the AAAI conference on artificial intelligence, V ol. 36. 8708–8716

2022
[54]

Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, and Fei Wu. 2020. Devlbert: Learning deconfounded visio-linguistic representations. In Proceedings of the 28th ACM International Conference on Multimedia. 4373–4382

2020
[55]

Wenqiao Zhang, Zheqi Lv, Hao Zhou, Jia-Wei Liu, Juncheng Li, Mengze Li, Yunfei Li, Dongping Zhang, Yueting Zhuang, and Siliang Tang. 2024. Revisiting the domain shift and sample uncertainty in multi-source active domain transfer. In Proceedings of the IEEE/CVF Conference on Co...

2024
[56]

Litian Zhang, Xiaoming Zhang, Chaozhuo Li, Ziyi Zhou, Jiacheng Liu, Feiran Huang, and Xi Zhang. 2024. Mitigating social hazards: Early detection of fake news via diffusion-guided propagation path generation. InProceedings of the 32nd ACM International Conference on Multimedia

2024
[57]

Qiang Zhang, Jiawei Liu, Fanrui Zhang, Jingyi Xie, and Zheng-Jun Zha. 2024. Natural language-centered inference network for multi-modal fake news detection. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24

2024
[58]

Wenqiao Zhang, Siliang Tang, Yanpeng Cao, Shiliang Pu, Fei Wu, and Yueting Zhuang. 2019. Frame augmented alternating attention network for video question answering. IEEE Transactions on Multimedia (2019)

2019
[59]

Wenqiao Zhang, Lei Zhu, James Hallinan, Shengyu Zhang, Andrew Makmur, Qingpeng Cai, and Beng Chin Ooi. 2022. Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation. In Proceedings of the IEEE/CVF Conference on ...

2022
[60]

Wenqiao Zhang, Haochen Shi, Jiannan Guo, Shengyu Zhang, Qingpeng Cai, Juncheng Li, Sihui Luo, and Yueting Zhuang. 2022. Magic: Multimodal relational graph adversarial inference for diverse and unpaired text-based image captioning. In Proceedings of the AAAI Conference on Artif...

2022
[61]

Wenqiao Zhang, Haochen Shi, Siliang Tang, Jun Xiao, Qiang Yu, and Yueting Zhuang. 2021. Consensus graph representation learning for better grounded image captioning. In Proceedings of the AAAI Conference on Artificial Intelligence

2021
[64]

Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2017. Exploiting context for rumour detection in social media. In Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part I 9. Springer

2017
[2023]

In The Twelfth International Conference on Learning Representations

Querying Easily Flip-flopped Samples for Deep Active Learning. In The Twelfth International Conference on Learning Representations
[2024]

In Proceedings of the AAAI conference on artificial intelligence

Panoptic scene graph generation with semantics-prototype learning. In Proceedings of the AAAI conference on artificial intelligence

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Yahui Chen. 2015. Convolutional neural network for sentence classification . Master’s thesis. University of Waterloo

work page 2015

[2] [2]

Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, and Chang D Yoo

work page

[3] [3]

Chaoqun Cui and Caiyan Jia. 2025. Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning. In Proceedings of the 31st International Conference on Computational Linguistics

work page 2025

[4] [4]

Zhenlong Dai, Bingrui Chen, Zhuoluo Zhao, Xiu Tang, Sai Wu, Chang Yao, Zhipeng Gao, and Jingyuan Chen. 2025. Less is More: Adaptive Program Repair with Bug Localization and Preference Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, V ol. 39. 128–136

work page 2025

[5] [5]

Zhenlong Dai, Chang Yao, WenKang Han, Yuanying Yuanying, Zhipeng Gao, and Jingyuan Chen. 2024. MPCoder: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3765–3780

2024

[6] [6]

Yiqi Dong, Dongxiao He, Xiaobao Wang, Youzhu Jin, Meng Ge, Carl Yang, and Di Jin. 2024. Unveiling implicit deceptive patterns in multi-modal fake news via Adaptation Method for Misinformation Identification Conference acronym ’XX, October 27–31, 2025, Dublin, Ireland neuro-symbolic reasoning. In Proceedings of the AAAI Conference on Artificial Intelligenc...

work page 2024

[7] [7]

Zhekai Du and Jingjing Li. 2023. Diffusion-based probabilistic uncertainty esti- mation for active domain adaptation. Advances in Neural Information Processing Systems 36 (2023), 17129–17155

work page 2023

[8] [8]

Fujun Han, Peng Ye, Shukai Duan, and Lidan Wang. 2024. Ada-iD: Active Domain Adaptation for Intrusion Detection. In Proceedings of the 32nd ACM International Conference on Multimedia. 7404–7413

work page 2024