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
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
Items 101–175 of 175
Learning by Hallucinating: Vision-Language Pre-training with Weak Supervision
Wang, T.-J. J., Laaksonen, J. T., Langer, T., Arponen, H., & Bishop, T. E. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00113 · Cited by 3
Knowledge-Embedded Mutual Guidance for Visual Reasoning
Zheng, W., Yan, L., Chen, L., Li, Q., & Wang, F. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2023.3310892 · Cited by 3
VCD: Visual Causality Discovery for Cross-Modal Question Reasoning
Liu, Y., Tan, Y., Luo, J., & Chen, W. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8540-1_25 · Cited by 3
Few-shot Named Entity Recognition via encoder and class intervention
Ding, L., Ouyang, C., Liu, Y., Tao, Z., Wan, Y., & Gao, Z. (2024) · AI Open · doi:10.1016/j.aiopen.2024.01.005 · Cited by 5
Event extraction for visual: De-biasing with causality-guided attention mechanism
Wang, C., Yang, J., & He, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130783 · Cited by 3
Advancing Causal Intervention in Image Captioning With Causal Prompt
Yu, Y., Kim, Y., & Ro, Y. M. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3487200 · Cited by 2
Causal-inspired learning-based method for unbiased pest recognition
Liu, Z., Ji, J., Zhang, Y., Dong, S., Zhang, Y., Lin, C., & Teng, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128774 · Cited by 2
Liu, Y., Shen, Z., Yang, H., Liu, W., & Yang, X. (2026) · Remote Sensing · doi:10.3390/rs18020358 · Cited by 2
Self-citationTarget-Driven Visual Navigation Using Causal Intervention
Zhao, X., Wang, T., Liu, K., Zhang, B., Li, C., & Snoussi, H. (2023) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc58219.2023.10327097 · Cited by 3
Improving Vision-and-Language Reasoning via Spatial Relations Modeling
Yang, C., Xu, R., Guo, Y., Huang, P., Chen, Y., Ding, W., Wang, Z., & Zhou, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00082 · Cited by 4
Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
Chu, Z., Ding, H., Zeng, G., Wang, S., & Li, Y. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3680073 · Cited by 3
Can We Debias Multimodal Large Language Models via Model Editing?
Wang, Z., Li, X., Qin, Z., Li, C., Tu, Z., Chu, D., & Sui, D. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681589 · Cited by 3
Zhang, J., Luo, X., & Xie, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113180 · Cited by 2
KMG-LL: Knowledge-enhanced Multimodal Graph for Dialogue Generation
Dong, Y., He, T., Dong, Q., & Qin, K. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890839 · Cited by 2
Counterfactual learning and saliency augmentation for weakly supervised semantic segmentation
Ding, X., Shao, Y., Tian, N., Wang, L., & Zhao, W. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105523 · Cited by 2
MFSVFND: Multimodal Fusion Network for Detecting Fake News on Short Video Platforms
Zhang, L., Yajing, Y., Yang, Y., Liu, Y., Gui, Z., Li, R., & Fei, H. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733486 · Cited by 2
A Causal Intervention Method for Domain Generalization with a Self-Supervised Auxiliary Task
Gong, Q., Pan, Y., Lai, H., & Yin, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02529-w · Cited by 2
Chen, T., Li, Z., Xu, P., & Zheng, H. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02052-y · Cited by 2
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
Chen, T., Liu, H., Wang, Y., Chen, Y., He, T., Gan, C., He, H., & Lin, W. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3626549 · Cited by 2
Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition
Wang, J., Li, H., Huo, G., Li, C., & Wei, Y. (2023) · Electronics · doi:10.3390/electronics12040791 · Cited by 2
Causal Intervention for Few-Shot Hypothesis Adaptation
Qi, G., Long, Y., Lu, Z., & Yu, H. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3306252 · Cited by 2
Self-citationDiverse image search with explanations
Zhu, X., & Liu, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16393-8 · Cited by 1
Multi-Dilated Transformer for Multimodal Sentiment Analysis
Xie, S., Hu, Z., Xie, X., Mao, Y., Zhen, X., & Zeng, X. (2025) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit65432.2025.11035582 · Cited by 1
Causal Inference Hashing for Long-Tailed Image Retrieval
Jin, L., Lu, Z., Li, Z., Pan, Y., Dai, L., Tang, J., & Jain, R. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3588054 · Cited by 1
Zhang, Y., Chen, Y., Yu, H., Dai, J., Liu, B., Li, N., Wang, C., & Elazab, A. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3605332 · Cited by 1
Disentangling response sequences with causal invariance for knowledge tracing
Hu, S., Hu, J., Yang, H., Geng, J., Ou, X., Xu, Z., Wang, H., & Huang, T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113380 · Cited by 2
Ma, J., Wang, J., Zhang, M., & Zhou, G. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755805 · Cited by 3
Towards explainable visual question answering via cross-modal causal reasoning
Li, W., Deng, F., & Li, Z. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113765 · Cited by 1
CFSM: A Novel Causal Feature Selection Module for Two-Dimensional Out-of-Distribution Generalization
Zhu, L., Yin, W., Yang, Y., Wu, Y., Gu, Q., Wang, X., Zhou, C., & Ye, N. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3616163 · Cited by 1
Zhang, M., Huang, L., Tang, B., Pu, S., & Tonggen, 杨. Y. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3651710 · Cited by 1
Shield Machine Attitude Prediction Method Based on Causal Graph Convolutional Network
Zeng, L., Yan, X., Zhang, C., Chen, Y., Su, W., & Wang, S. (2026) · Algorithms · doi:10.3390/a19030224 · Cited by 1
Mutually Causal Semantic Distillation Network for Zero-Shot Learning
Chen, S., Chen, S., Xie, G.-S., & You, X. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02814-2 · Cited by 1
CausalCompNet: Causal intervention meets vision-language priors for robust CXR diagnosis
Liu, M., Li, Q., Chang, R., Xu, Z., Zhou, C., & Nie, W. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110554 · Cited by 1
Zhang, R., Zhao, Q., Hao, C., Dong, W., Hou, P., Chen, Q., Li, F., & Zhang, X. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.111910 · Cited by 1
MCAF: Multi-Headed Causal Attention Fusion Approach for Multimodal Sentiment Analysis
Wang, Q., Wang, X., Yang, J., & Bi, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650874 · Cited by 1
Towards Deconfounded Visual Question Answering via Dual-causal Intervention
Peng, D., & Wei, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679594 · Cited by 1
Uncovering Causal Relationships for Debiased Repost Prediction Using Deep Generative Models
Sun, W.-J., & Liu, X. F. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.057714 · Cited by 1
Luo, J., Wang, J., Ma, J., Jin, Y., Li, S., & Zhou, G. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714642 · Cited by 1
Causalseg: investigating causality modeling for semi-supervised video object segmentation
Zhang, Z., Li, N., Wang, W., & Guo, H. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01825-2 · Cited by 1
Data Efficient Masked Language Modeling for Vision and LanguagePreprint
Bitton, Y., Stanovsky, G., Elhadad, M., & Schwartz, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.02040 · Cited by 3
A Survey on Causal Inference in Image Captioning
Kim, J., & Kim, J. (2023) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic57457.2023.10049869
Multi-source Interaction Network for TextVQA
Han, M., & Jin, T. (2022) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc56324.2022.10065913
CauSim: A Causal Learning Framework for Fine-Grained Image Similarity
Debbi, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43078-7_3
VGGAN: Visual Grounding GAN Using Panoptic Transformers
Quan, F., & Lang, B. (2023) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc58118.2023.10270121
Ness, P. M., Marijan, D., & Bose, S. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614960
Deconica: Deconfounding the Dataset Bias for Domain GeneralizationPreprint
Sheng, S., Shu, X., & Yan, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4616994
Causal Interventional Time Series Forecasting on Multi-horizon and Multi-series Data
Chu, Z., Li, R., & Li, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-35051-1_12
Voice-Driven Virtual Population Type Method Based on Attention Mechanism
RuoFei, C., Runyun, Y., JingCheng, W., & JiChao, L. (2023) · International Conference on Information Communication and Signal Processing (ICICSP) · doi:10.1109/icicsp59554.2023.10390628
A multimodal sentiment model based on causal gating attention mechanismPreprint
Gan, Z., Sun, X.-E., & Liu, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3932545/v1
DeconICA: Deconfounding the Dataset Bias for Domain GeneralizationPreprint
Sheng, S., Shu, X., & Yan, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4768682
Unbiased Image Caption Generation Based on Dynamic Counterfactual Inference
Zhou, J., Yang, C. Z., Zhu, Y., & Zhang, Y. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-3623-2_11
Deconfounded Fashion Image Caption with Transformer and RetrievalPreprint
Peng, T., Yin, W., Liu, J., Li, L., & Hu, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4871168
Overparametrization helps offline-to-online generalization of closed-loop control from pixels
Lechner, M., Hasani, R., Amini, A., Wang, T.-H. J., Henzinger, T. A., & Rus, D. L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610284
DTAFORMER: Directional Time Attention Transformer For Long-Term Series Forecasting
Chang, J., Yue, L., & Liu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8505-6_12
SVVD-NET: A framework with relative position constraints for vertebral vertex detection
Xu, Y., Duan, L., Zhang, Z., Sun, T., Zhang, Y., & Tian, L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107746
Cci: A Causal Counterfactual Inference-Based Method for Object State ChangesPreprint
Wang, Z., Huang, S., Zhang, Z., & Li, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5202372
Image Descriptions for Visually Impaired Individuals to Locate Restroom Facilities
He, C.-S., Lo, N.-K., Chien, Y.-H., & Lin, S.-S. (2025) · IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE) · doi:10.3390/engproc2025092013 · Cited by 1
Causality-Adjusted Data Augmentation for Domain Continual Medical Image Segmentation
Zhu, Z., Dong, Q., Luo, G., Wang, W., Dong, S., Wang, K., Tian, Y., Wang, G., & Li, S. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3584068
Causality guided co-attention network for visual question answering
Miao, J., Yu, K., Fang, B., Le, W., & Hong, R. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108200
Towards Robust Visual Question Answering via Causal Intervention and Contrastive Learning
Li, W., & Li, Z. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11208923
EmbDiffounder: Semantic-Transfer Enhanced Sequence Diffusion Model for Text Generation
Huang, J., Zheng, D., Li, J., & Pang, P. C. (2025) · IEEE Access · doi:10.1109/access.2025.3638810 · Cited by 1
STATE-Of-The-Art Causal AI Applications in NLP, CV, and Time-Series Data
Elidrissi, A., & Sabri, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07915-2_27
FD-DeCap: A Front-Door Causal Inference-Based Framework for Debiasing Automatic Audio Captioning
Liu, J., Li, H., Wei, M., Ji, Z., Zhang, H., & Ганчев, И. (2026) · IEEE Access · doi:10.1109/access.2026.3651636
Self-citationDCCR: Debiasing Cross-Document Event Coreference Resolution with Counterfactual Reasoning
Yao, L., Yang, W., Yin, Y., Wei, F., Lv, H., Wang, L., & Tao, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4987-0_38
ACP-MSP: Adaptive Causal Planning With Integrated Multiscale Prediction for Autonomous Driving
Gao, F., Wang, Z., Chang, S., Gao, Z., Zhao, R., Zheng, C., & Yang, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3661551
Li, X., Zhu, M., Yang, C., Zhang, K., Wang, X., Aboyeji, S. T., Chen, F., Yao, C., Fan, X.-D., Zeng, X., et al. (2026) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2026.3668888
Time-Causal VAE: Robust Financial Time Series Generator
Acciaio, B., Eckstein, S., & Hou, S. (2026) · SIAM Journal on Financial Mathematics · doi:10.1137/24m1711650 · Cited by 15
Vision-Language Integration for Image Captioning Using Vision Transformers and GPT-J
Alfatemi, A., Rahouti, M., Aledhari, M., Ghani, N., Chehri, A., & Jeon, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-18474-0_8
Quadruplet Augmentation With Attribute and Structure Invariance for Online Continual Learning
Wu, J., Wang, S., Sun, Y., Wang, B., & Yin, B. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3681470
Causal Counterfactual Inference Network for Video Object State Changes in Open-World Scenarios
Wang, Z., Huang, S., Li, M., & Jiao, Y. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3678945
Liu, L., Zhang, Y., Wang, J., Xu, D., & Zhang, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464008
Debiased Multimodal Personality Understanding through Dual Causal Intervention
Zhu, Y., Han, Z., Ning, N., Wei, Y., Wang, Y., Feng, H., & Shao, Z. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809547
Zhang, W., Ramezani, R., Han, T., Hwang, K., & Guo, M. (2026) · The Visual Computer · doi:10.1007/s00371-026-04584-x
Critiquing AI Surveys with a Case Study on Temporal Features for Trustworthy Computer Vision
Napier, J., Wang, Z., Dong, J. S., Ross, K. J., Chen, S., & Hóu, Z. (2026) · ACM Computing Surveys · doi:10.1145/3849866
Zhai, H., Ding, Y., Wang, X., Xiang, Y., Tong, X., & Bu, F. (2026) · Computer Speech & Language · doi:10.1016/j.csl.2026.102065
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
