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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.
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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
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- 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
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A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao
Meng, Y., Guo, C., Cao, Y., Liu, T., & Zheng, B. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3731935 · Cited by 6
Enhancing Vision-Language Pre-Training with Rich Supervisions
Gao, Y., Shi, K., Zhu, P., Belval, E., Nuriel, O., Appalaraju, S., Ghadar, S., Tu, Z., Mahadevan, V., & Soatto, S. (2024) · 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/cvpr52733.2024.01280 · Cited by 7
Causal-driven Large Language Models with Faithful Reasoning for Knowledge Question Answering
Wang, J., Cao, D., Lu, S., Ma, Z., Xiao, J., & Chua, T. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681263 · Cited by 6
Collaborative Aware Bidirectional Semantic Reasoning for Video Question Answering
Wu, X., Wu, J., Zhu, L., Senhadji, L., & Shu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3490665 · Cited by 7
CAGCN: Causal attention graph convolutional network against adversarial attacks
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Causal-TSF: A Causal Intervention Approach to Mitigate Confounding Bias in Time Series Forecasting
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Deconfounded fashion image captioning with transformer and multimodal retrieval
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Yao, R., Li, K., Dou, Y., Xu, Z., & Wang, M. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3431243 · Cited by 5
CRViT: Vision transformer advanced by causality and inductive bias for image recognition
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Do learned representations respect causal relationships?
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Overcoming Language Priors via Shuffling Language Bias for Robust Visual Question Answering
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LL-Diff: Low-Light Image Enhancement Utilizing Langevin Sampling Diffusion
Boren, D. I. N. G., Zhang, X., Yu, Z., & Hui, Z. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424540132 · Cited by 5
Zhu, L., Yang, Y., Nie, Z., Gao, Y., Li, J., Gu, Q., Wang, X., Zhou, C., & Ye, N. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3595676 · Cited by 5
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Huang, P.-H., Li, J.-L., Chen, C.-P., Chang, M., & Chen, W.-C. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00597 · Cited by 7
Context-Assisted Attention for Image Captioning
Lian, Z., Wang, R., Li, H., & Hu, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15919-0_60 · Cited by 4
Zheng, X., Luo, X., & Liu, D. (2025) · IEEE Power Electronics Magazine · doi:10.1109/mpel.2025.3577116 · Cited by 3
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 ›
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