The DOI matches its Crossref record; 81 of the 83 references it cites were found in a registry or an index.
Causal Attention for Vision-Language Tasks
Abstract
We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. This effect causes harmful bias that misleads the attention module to focus on the spurious correlations in training data, damaging the model generalization. As the confounder is unobserved in general, we use the front-door adjustment to realize the causal intervention, which does not require any knowledge on the confounder. Specifically, CATT is implemented as a combination of 1) In-Sample Attention (IS-ATT) and 2) Cross-Sample Attention (CS-ATT), where the latter forcibly brings other samples into every IS-ATT, mimicking the causal intervention. CATT abides by the QK-V convention and hence can replace any attention module such as top-down attention and self-attention in Transformers. CATT improves various popular attention-based vision-language models by considerable margins. In particular, we show that CATT has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT [57], which uses fewer data and less computational power, comparable to the heavier UNITER [14]. Code is published in https://github.com/yangxuntu/lxmertcatt.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Meanwhile, overly compound pretraining objectives potentially upgrade the challenge of balancing among different loss terms, thus complicate the training optimization [199].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [199]
In the citing paper
“Some recently successful practice [9], [199], [205] used weakly aligned image-text pairs to perform pretraining, and achieve both competitive performance and zero-shot learning capability for image classification, image-text retrieval, and open-ended visual question answering, etc.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [199]
In the citing paper
“Some recent attempts [137], [199] study the use of weakly-aligned cross-modal supervision to train Transformers to learn the cross-modal interactions.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [199]
In the citing paper
“existing multimodal Transformer models [9], [199], [263] are superior only for specific MML tasks, as they are designed specifically for only a subset of specific tasks [137], [142], [212], [249], [260], [261], [265], [266].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [199]
In the citing paper
“As the learning scale expands exponentially, the training data become inevitably noisy and heterogeneous [9], [199], [263].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [199]
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 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) · doi:10.18653/v1/n19-1423 · Cited by 54,325
(2014) · Cited by 33,419
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
(2004) · Cited by 9,718
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00636 · Cited by 5,416
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
(2005) · Cited by 4,152
Bleu: a method for automatic evaluation of machine translation
Papineni, K. (2024) · doi:10.57702/ljteqmrb · Cited by 3,668
Causality: models, reasoning, and inference
(2000) · Choice Reviews Online · doi:10.5860/choice.38-1494a · Cited by 2,968
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · cited 4×
“Fortunately, the front-door adjustment [43] does not require any knowledge on the confounder and can also calculate the causal effect between X and Y in a front-door SCM as in Figure 8(b).”
BackgroundInfluentialLXMERT: Learning Cross-Modality Encoder Representations from Transformers
Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1514 · Cited by 2,319
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.670 · Cited by 2,287
Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
Rubin, D. B. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001880 · Cited by 2,173
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv · doi:10.48550/arxiv.1908.02265 · Cited by 2,109
Stacked Attention Networks for Image Question Answering
Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.10 · Cited by 2,072
UNITER: UNiversal Image-TExt Representation Learning
Chen, Y.-C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., & Liu, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58577-8_7 · Cited by 2,055
SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1238 · Cited by 1,879
Image Captioning with Semantic Attention
You, Q., Jin, H., Wang, Z., Chen, F., & Luo, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.503 · Cited by 1,869
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Li, X., Yin, X. P., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Li, D., Wei, F., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_8 · Cited by 1,791
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614
Causal Inference in Statistics
Pearl, J., Glymour, M., & Jewell, N. P. (2016) · Cited by 1,436
Hierarchical Question-Image Co-Attention for Visual Question AnsweringPreprint
Lu, J., Yang, J., Batra, D., & Parikh, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00061 · Cited by 1,357
Meshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259
VisualBERT: A Simple and Performant Baseline for Vision and Language
Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., & Chang, K.-W. (2019) · arXiv · doi:10.48550/arxiv.1908.03557 · Cited by 1,198
Attention on Attention for Image Captioning
Huang, L., Wang, W., Chen, J., & Wei, X.-Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00473 · Cited by 1,092
The Dynamic Representation of Scenes
Rensink, R. A. (2000) · Visual Cognition · doi:10.1080/135062800394667 · Cited by 1,089
Deep Modular Co-Attention Networks for Visual Question Answering
Zhou, Y., Yu, J., Cui, Y., Tao, D., & Tian, Q. C. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00644 · Cited by 1,033
(2020) · International Conference on Machine Learning · Cited by 1,012
Exploring Visual Relationship for Image Captioning
Yao, T., Pan, Y., Li, Y., & Mei, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_42 · Cited by 967
Unified Vision-Language Pre-Training for Image Captioning and VQA
Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J., & Gao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7005 · Cited by 864
Auto-Encoding Scene Graphs for Image Captioning
Yang, X., Tang, K., Zhang, H., & Cai, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01094 · Cited by 860
Self-citationUnbiased Scene Graph Generation From Biased Training
Tang, K., Niu, Y., Huang, J., Shi, J., & Zhang, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00377 · Cited by 810
Self-citationMUTAN: Multimodal Tucker Fusion for Visual Question Answering
Ben-younes, H., Cadène, R., Cord, M., & Thome, N. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.285 · Cited by 753
The Retinotopy of Visual Spatial Attention
Tootell, R. B. H., Hadjikhani, N. K., Hall, E., Marrett, S., Vanduffel, W., Vaughan, J. T., & Dale, A. M. (1998) · Neuron · doi:10.1016/s0896-6273(00)80659-5 · Cited by 735
Hadamard Product for Low-rank Bilinear PoolingPreprint
Kim, J.-H., On, K.-W., Lim, W., Kim, J., Ha, J.-W., & Zhang, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.04325 · Cited by 702
StereoSet: Measuring stereotypical bias in pretrained language models
Nadeem, M., Bethke, A., & Reddy, S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.416 · Cited by 643
A Corpus for Reasoning about Natural Language Grounded in Photographs
Suhr, A., Zhou, S., Zhang, A. Q., Zhang, I., Bai, H., & Artzi, Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1644 · Cited by 634
The Pareto, Zipf and other power laws
Reed, W. J. (2001) · Economics Letters · doi:10.1016/s0165-1765(01)00524-9 · Cited by 625
Counterfactual VQA: A Cause-Effect Look at Language Bias
Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X., & Wen, J.-R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01251 · Cited by 464
Self-citationExternal Validity: From Do-Calculus to Transportability Across Populations
Pearl, J., & Bareinboim, E. (2014) · Statistical Science · doi:10.1214/14-sts486 · Cited by 455
Object Hallucination in Image Captioning
Rohrbach, A., Hendricks, L. A., Burns, K., Darrell, T., & Saenko, K. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1437 · Cited by 420
Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering
Gao, P., Jiang, Z., You, H., Lu, P., Hoi, S. C. H., Wang, X., & Li, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00680 · Cited by 420
12-in-1: Multi-Task Vision and Language Representation Learning
Lu, J., Goswami, V., Rohrbach, M., Parikh, D., & Lee, S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01045 · Cited by 420
Women Also Snowboard: Overcoming Bias in Captioning Models
Hendricks, L. A., Burns, K., Saenko, K., Darrell, T., & Rohrbach, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_47 · Cited by 404
External validity: From do-calculus to transportability across populations
Pearl, J., & Bareinboim, E. (2013) · Cited by 398
Hierarchical Question-Image Co-Attention for Visual Question Answering
Lu, J. (2024) · TIB Data Manager · doi:10.57702/uhdmfu1r · Cited by 357
Causal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353
Self-citationMeasuring Bias in Contextualized Word Representations
Kurita, K., Vyas, N., Pareek, A., Black, A. W., & Tsvetkov, Y. (2019) · Proceedings of the First Workshop on Gender Bias in Natural Language Processing · doi:10.18653/v1/w19-3823 · Cited by 348
Wang, T., Huang, J., Zhang, H., & Sun, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01077 · Cited by 291
Self-citationInterventional Few-Shot LearningPreprint
Yue, Z., Zhang, H., Sun, Q., & Hua, X.-S. (2020) · Neural Information Processing Systems · doi:10.48550/arxiv.2009.13000 · Cited by 289
Self-citationCounterfactual Zero-Shot and Open-Set Visual Recognition
Yue, Z., Wang, T., Sun, Q., Hua, X., & Zhang, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01515 · Cited by 221
Self-citationDistilling Causal Effect of Data in Class-Incremental Learning
Hu, X., Tang, K., Miao, C., Hua, X., & Zhang, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00395 · Cited by 201
Self-citationLearning to Count Objects in Natural Images for Visual Question Answering
Zhang, Y., Hare, J. S., & Prügel‐Bennett, A. (2018) · ePrints Soton (University of Southampton) · doi:10.48550/arxiv.1802.05766 · Cited by 194
Discriminability Objective for Training Descriptive Captions
Luo, R., Shakhnarovich, G., Cohen, S. D., & Price, B. L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00728 · Cited by 194
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., & Pal, C. (2019) · arXiv · doi:10.48550/arxiv.1901.10912 · Cited by 182 · cited 1×
“Causality [44, 56] provides researchers new methodologies to design robust measurements [60], discover hidden causal structures [9], generate counterfactual samples [62, 1, 27, 80], and confront various biases [67, 81, 83, 41, 20, 48].”
BackgroundLook Back and Predict Forward in Image Captioning
Qin, Y. X., Du, J., Zhang, Y., & Lu, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00856 · Cited by 153
Two Causal Principles for Improving Visual Dialog
Qi, J., Niu, Y., Huang, J., & Zhang, H. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01087 · Cited by 151
Self-citationCounterfactual Vision and Language Learning
Abbasnejad, E., Teney, D., Parvaneh, A., Shi, Q., & van den Hengel, A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01006 · Cited by 136
CausalGAN: Learning Causal Implicit Generative Models with Adversarial TrainingPreprint
Kocaoglu, M., Snyder, C., Dimakis, A. G., & Vishwanath, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02023 · Cited by 128
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene GraphPreprint
Yu, F. R., Tang, J., Yin, W., Sun, Y., Tian, H., Wu, H. R., & Wang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.16934 · Cited by 106
Adapting Text Embeddings for Causal InferencePreprint
Veitch, V., Sridhar, D., & Blei, D. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12741 · Cited by 90
GQA: a new dataset for compositional question answering over real-world images.Preprint
Hudson, D. A., & Manning, C. D. (2019) · arXiv (Cornell University) · Cited by 81
Powell, S. G. (2018) · Journal of MultiDisciplinary Evaluation · Cited by 71
Suter, R., Miladinovic, D., Schölkopf, B., & Bauer, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00007 · Cited by 41
SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple LevelsPreprint
Li, C., Yan, M., Xu, H., Luo, F., Wang, W., Bi, B., & Huang, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07829 · Cited by 27
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering
Yang, X., Lin, G., Lv, F., & Liu, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_25 · Cited by 26
Self-citation
Research works printed but not linked: 3
- luo, 2017, An Image Captioning Codebase in Pytorch
- hu, 2021, Distilling causal effect of data in classincremental learning
- radford, 2018, Improving language understanding by generative pre-training
Cited by
Sorted by citations
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
Counterfactual VQA: A Cause-Effect Look at Language Bias
Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X., & Wen, J.-R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01251 · Cited by 464
Self-citationTransVG: End-to-End Visual Grounding with Transformers
Deng, J., Yang, Z., Chen, T., Zhou, W., & Li, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00179 · Cited by 424
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering
Liu, Y., Li, G., & Lin, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3284038 · Cited by 373
Counterfactual Zero-Shot and Open-Set Visual Recognition
Yue, Z., Wang, T., Sun, Q., Hua, X., & Zhang, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01515 · Cited by 221
Self-citation
Metadata
- Authors
- Yang, X., Zhang, H., Qi, G., & Cai, J.
- Affiliations
- Nanyang Technological University (SG)
- Monash University (AU)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 9842-9852
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3171353004
- Cited by
- 175
- References
- 83
- Citations by year
- 2026: 22 · 2025: 39 · 2024: 49 · 2023: 40 · 2022: 17 · 2021: 8
- FWCI
- 9.32
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- causal attention, vision-language models, spurious correlations, confounding, front-door adjustment, causal intervention, LXMERT, pre-training, model generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
