The DOI matches its Crossref record; 89 of the 91 references it cites were found in a registry or an index.
Visual Commonsense R-CNN
Open accessAbstract
We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also the core reason why VC R-CNN can learn “sense-making” knowledge like chair can be sat - while not just “common” co-occurrences such as chair is likely to exist if table is observed. We extensively apply VC R-CNN features in prevailing models of three popular tasks: Image Captioning, VQA, and VCR, and observe consistent performance boosts across them, achieving many new state-of-the-arts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“The backdoor intervention utilizes a do-operator to disrupt the dependency between the source object and other context objects , which are considered the potential confounders affecting the association assessment between the object and .”
From Unbiased Embodied Visual Representation Learning with Causal Inference and Cross-Modality Alignment (2025) · cites this paper as Wang et al. [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
(2008) · Journal of Machine Learning Research · Cited by 33,051
Research works printed but not linked: 1
- kalainathan, 2018, Sam Structural agnostic model causal discovery and penalized adversarial learning
Cited by
Sorted by citations
Items 201–291 of 291
Pixels to Prose: Understanding the Art of Image CaptioningPreprint
Sharma, A., Singh, H., & Pant, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5351410 · Cited by 4
CITAL: Counterfactual intervention for temporal action localization with point-level annotation
Hu, Y., Xia, Z., Chen, Z., Tsering, T., Cheng, J., & Tashi, N. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130006 · Cited by 3
Causal view mechanism for adversarial domain adaptation
Fu, Z., Wang, S., Zhao, X. T., Long, S., & Wang, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15683-5 · Cited by 3
Ou, W., Chen, Y., Liang, L., Gou, J., Xiong, J., Zhang, J., Lai, L., & Zhang, L. (2025) · Multimedia Systems · doi:10.1007/s00530-024-01649-6 · Cited by 4
Robust Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning
Qi, M., Lv, C., & Ma, H. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3627224 · Cited by 3
Adaptive Re-Balancing Network with Gate Mechanism for Long-Tailed Visual Question Answering
Chen, H., Liu, R., Fang, H., & Zhang, X. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414074 · Cited by 3
Alleviating Matching Bias in Marketing Recommendations
Fang, J.-P., Cui, Q., Zhang, G.-D., Tang, C., Gu, L., Li, L., Gu, J., Zhou, J., & Wu, F. Q. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591854 · Cited by 3
Toward Quantifiable Face Age Transformation Under Attribute Unbias
Lin, L., Wang, T., Liu, H., Zhu, C., & Chen, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3422661 · Cited by 3
Self-citationZhu, J., Wang, H., & Shi, M. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3428487 · Cited by 3
Le, T., Huy, N. T., & Minh, N. L. (2020) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse50997.2020.9287539 · Cited by 4
A causality-inspired feature selection method for cancer imbalanced high-dimensional dataPreprint
Liu, Y., Huang, Q., Sun, H., & Chang, Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.04.462984 · Cited by 3
CausalFD: causal invariance-based fraud detection against camouflaged preference
Song, Y., Wei, Y.-C., Yuan, H.-N., Sun, Q.-Y., Fu, X., Wang, L.-E., & Li, X. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02209-0 · Cited by 4
Self-citationAdvancing 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
Ma, X., Fang, X., Luo, G., Qiu, X., Liu, X., Huang, C., Wang, K., Qiu, Z., Zhang, T., Li, Y., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3640646 · Cited by 2
Wang, H. J., & Sun, L. (2025) · Applied Sciences · doi:10.3390/app152413008 · Cited by 2
Huang, X., Zhang, H., Ge, J., & Tang, W. (2026) · IEEE Access · doi:10.1109/access.2026.3653958 · Cited by 4
Self-citationBidirectional causal learning for visual question answering
Long, F., Wei, P., Li, P., Jiang, M., & Li, W. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.105955 · Cited by 2
Vision-Language Interactive Relation Mining for Open-Vocabulary Scene Graph Generation
Min, Y., Yang, M., Zhang, J., Wang, Y., Wu, A., & Deng, C. D. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01556 · Cited by 4
An Active Learning Algorithm for Image Classification Based on Difficulty and Competence
Li, G., Zhao, L., & Gu, J. (2023) · IEEE Access · doi:10.1109/access.2023.3279717 · Cited by 2
Target-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
Self-citationA Comprehensive Overview of CFN From a Commonsense Perspective
Li, R., Zhao, Y., Wang, Z., Su, X., Guo, S., Guan, Y., Han, X., & Zhao, H. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1450-8 · Cited by 2
On Consistency Training for Language-Based Image Editing Interface
Lee, Y., Lee, A., Jung, Y., & Hwang, S.-W. (2023) · doi:10.18653/v1/2023.nlint-1.2 · Cited by 2
Yu, T., & Wu, W. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9d66 · Cited by 2
A Comprehensive Analysis of Methodologies used for Object Detection and Localization
Delight, D. T., & Karunakaran, V. (2021) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs51430.2021.9441916 · Cited by 2
DeVLBert: Out-of-distribution Visio-Linguistic Pretraining with Causality
Zhang, S., Jiang, T., Wang, T., Kuang, K., Zhao, Z., Zhu, J., Yu, J., Yang, H., & Wu, F. Q. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00191 · Cited by 2
Self-citationNguyen-Tran, D.-M., Le, T., Pho, K., Nguyen, L.-M., & Nguyen, H. T. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-16210-7_35 · Cited by 2
Towards Causality Inference for Very Important Person Localization
Wang, X., Wang, Z., Liu, W., Xu, X., Zhao, Q., & Satoh, S. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548014 · Cited by 2
A Multi-Layer Attention Network for Visual Commonsense Reasoning
Zhang, W., Gao, Y., Qian, H., & Lyu, H. (2022) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit55514.2022.9943834 · Cited by 2
Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation
Chen, W., Tian, J., Fan, C., Li, Y., He, H., & Jin, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26490 · Cited by 2
Robust Sentiment Classification Based on the Backdoor Adjustment
Dai, L., & Han, M. (2023) · International Conference on Machine Learning and Natural Language Processing (MLNLP) · doi:10.1145/3639479.3639488 · Cited by 1
Causal Intervention For Robust Classification
Luo, H. (2024) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice61279.2024.10499145 · Cited by 1
Causal Intervention and Semantic Knowledge for Object Relationships
Fears, A., Raglin, A. J., & Basak, A. (2024) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi62246.2024.00013 · Cited by 1
Temporal-based graph reasoning for Visual Commonsense Reasoning
Wu, S., Liu, K., Li, J., Chen, P., Zhang, X., & Feng, Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113214 · Cited by 1
Lai, Y., Wu, L., Lin, H., & Zhou, X. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00127 · Cited by 1
Visual Commonsense Causal Reasoning From a Still Image
Wu, X., Guo, R., Li, Q., & Zhu, N. (2025) · IEEE Access · doi:10.1109/access.2025.3558429 · Cited by 1
Front-door causal attention for unbiased panoptic scene graph generation
Liang, S., Zhang, L., Liu, B., Yan, S., & Zhu, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130823 · Cited by 1
DM-CATN: Deep Modular Co-Attention Transformer Networks for image captioning
Wang, X., Fang, X., & Yang, Y. (2022) · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP) · doi:10.1117/12.2659580 · Cited by 1
Model-Agnostic Causal Principle for Unbiased KPI Anomaly Detection
Ji, J., Guan, D., Deng, Y., & Yuan, W. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892664 · Cited by 1
Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling
Wu, J., Wang, R., Yu, T., Zhang, R., Zhao, H., Li, S., Henao, R., & Nenkova, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.251 · Cited by 3
Causal Inference and Natural Language Processing
Chen, W., & Chu, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-35051-1_9 · Cited by 2
Debiasing Medication Recommendation with Counterfactual Analysis
Tang, P. S., Ouyang, C., & Liu, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8141-0_32 · Cited by 2
De-Bias Using Abstract Meaning Representation for Image Captioning
Kim, J., Jeon, J., Jung, S., & Kim, J. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444443 · Cited by 1
Stress concentration identification based on YOLOv8n algorithm
Yang, X., Zhan, M., Che, S., & Wang, L. (2024) · International Conference on Algorithms, Software Engineering and Network Security (ASENS) · doi:10.1145/3677182.3677232 · Cited by 1
Multiple Instance Learning with Fine-grained Causal Intervention for Histopathological Diagnosis
Song, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651005 · 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
Integrating Markov Blanket Discovery Into Causal Representation Learning for Domain Generalization
Yin, N., Wang, H., Yu, Y., Gao, T., Dhurandhar, A., & Ji, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72684-2_16 · 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
Qin, Y., & Ai, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103237 · Cited by 1
Tang, L.-Z., Zhang, Z., Yang, J., Feng, Y., Wang, Q., Sun, S., & Shao, H. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3592359 · Cited by 2
Debiasing Event Understanding for Visual Commonsense Tasks
Seo, M., Jung, Y., Choi, S. L., Hwang, S.-W., & Liu, B. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.65 · Cited by 1
Counterfactual Causal Adversarial Networks for Domain Adaptation
Jia, Y., Zhang, X., Lan, L., & Luo, Z. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1645-0_58 · Cited by 1
Decoupled Visual Causality for Robust Detection
Jiang, P., Deng, X., & Zhang, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095202 · Cited by 1
Driver-centric Risk Object Identification.Preprint
Li, C., Chan, S. H., & Chen, Y. (2021) · arXiv (Cornell University) · Cited by 1
Unbiased Embodied Visual Representation Learning with Causal Inference and Cross-Modality Alignment
Kang, J., Chen, B., Zhong, P., Wang, Y., Yang, H., & Sheng, Y. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3760261
Free Lunch for Co-Saliency Detection: Context AdjustmentPreprint
Kong, L., Ganesh, P., Wang, T., Liu, J., Zhang, L., & Chen, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02093
Self-citationLin, J., Wang, K., Chen, Z., Liang, X., & Lin, L. (2021) · arXiv (Cornell University)
CIDEr-R: Robust Consensus-based Image Description EvaluationPreprint
dos Santos, G. O., Colombini, E. L., & Avila, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.13701 · Cited by 3
Critical Analysis of Deconfounded Pretraining to Improve Visio-Linguistic Models
Cornille, N., Laenen, K., & Moens, M.-F. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.736791 · Cited by 1
Causal Intervention Learning for Multi-person Pose Estimation
Yue, L., Li, J., & Liu, Q. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02375-0_14
Body part relation reasoning network for human activity understanding
Liu, Q., Che, X., & Zhao, M. (2022) · Information Sciences · doi:10.1016/j.ins.2022.11.012
Semantic-aware multi-branch interaction network for deep multimodal learning
Pan, H., & Huang, J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08048-w · Cited by 1
Self-citationA 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
A Novel Approach to Visual Linguistics by Assessing Multi-level Language Substructures
Arora, M., Mudgil, P., Kumar, R., Kapoor, T., Gupta, R., & Agnihotri, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7615-5_12
Controllable Image Caption Based on Adaptive Weight and Optimization Strategy
Shao, J., & Yang, R. (2023) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661822040125
Jia, J., Ma, S., Wang, L., Liu, Y., & Deng, R. H. (2023) · IEEE Transactions on Computers · doi:10.1109/tc.2023.3318403
DE-AGE Confounder Based Causal Representation Learning for Cuffless Blood Pressure Estimation
Fu, Z., Yao, S., Shen, Z., Chen, Y., & Ding, X. (2023) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip60502.2023.10387024
Open-Set Text Recognition: Concept, Dataset, Protocol, and Framework
Yin, X.-C., Yang, C. H., & Liu, C. (2024) · SpringerBriefs in computer science · doi:10.1007/978-981-97-0361-6_3
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
Peak response regularization for localization
Yu, J., Yao, J., Zhao, C., Zhao, X., & Hu, Q. (2024) · Scientific Reports · doi:10.1038/s41598-024-65770-2
SCC-CAM: Weakly Supervised Segmentation on Brain Tumor MRI with Similarity Constraint and Causality
Jiao, P., Tian, Z., Chen, Z., Guo, X., Chen, Z., Dou, L., & Du, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8490-5_19
Li, Q., Wu, J., Liu, X., Li, D., & Xue, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103692
Towards Precise Embodied Dialogue Localization via Causality Guided Diffusion
Wang, H., Wang, L., Zhou, S., Tian, J., Qin, Z., Wang, Y., Hua, G., & Tang, W. (2025) · 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/cvpr52734.2025.01246 · Cited by 1
AdaDCL: An Adaptive Disentangled Contrastive Recommendation Method
Liu, X., Zou, L., Zhang, X., Bouyer, A., & Meo, P. D. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3597081
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
Wang, Y., Liu, Y., Huang, X., Huang, Y., & Wang, Y. (2025) · International Symposium on IoT and Intelligent Robotics (IoTIR) · doi:10.1109/iotir66925.2025.00023
Causal Inference for Alleviating Sequence Mutation in Sequential Recommendation
Zhang, S., Wu, K., Hu, C., Liu, M., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5716-5_40
Yin, Y., Yang, J., & An, N. (2026) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2026.3665199
Foundational principles for representation learning: applications in autonomous mobility
Alahi, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26789-5.00015-8
Long-Tailed Fault Diagnosis of Tunnel Boring Machines Main Bearing Based on Causal Inference
Pang, Z., Gao, S., Zhang, Z., Guo, P., & Jiang, Y. (2025) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-xian66756.2025.11427716
Causal Decoupling of Spatio-Temporal Confounders for Classroom Interaction Behaviour Recognition
Sun, B., Hong, M., He, J., & Zhang, Y. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10563-0
A Causal Lens on Non-RGB Vision Sensor Understanding in Vision-Language Models
Yu, Y., & Ro, Y. M. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3675431
Causal Debiasing and Feature Fusion for Open Set Domain Adaptation
Lan, A. Y., Li, B. F., Lu, C. Y., Zhao, D. M., & Jin, E. C. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464284
Liu, B., He, W., Jiang, Y., Yu, Q., & Chen, F. (2026) · Mathematics · doi:10.3390/math14091521
Alarm Event Prediction Based on Structural Causal Model in Smart Substation
Lu, X., Chen, Y., Fu, Y., Ren, F., & Ma, Z. (2026) · Energies · doi:10.3390/en19102296
Human–Machine Hybrid Augmented Intelligence
Zheng, N., Xue, J., Lan, X., Du, S., Pan, G., Wang, J., Chen, B., & Wang, L. (2026) · Advanced topics in science and technology in China · doi:10.1007/978-981-95-8037-8_5
CARE: A Constraint-Aware Hypergraph Framework for Knowledge-Driven Event-Centric Retrieval
Yang, M., Li, X., Yang, Z., Zhang, J., Zhang, X., & Li, Y. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810679
Attention-Level Causal Intervention Framework for Multimodal Fake News Detection
Hao, S., Li, S., Lin, R., Zhang, J., & Wang, X. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090301
Lightweight Neural Modular Reasoning for Compositional Visual Question Answering
Kirti, S., Hamsini, V., Hande, D. M., Bhat, A., & Das, B. (2026) · International Conference on Graphics and Signal Processing (ICGSP) · doi:10.1109/icgsp70553.2026.11682746
Counterfactual reasoning and causal intervention for multi-modal rumor detection
Liu, Q., Cai, G., Bi, G., & Qiu, Z. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-026-02875-2
Liu, J., Yao, R., Yang, Q., Liu, R., Hu, J., Lyu, J., Wang, Z., Zhang, Z., Feng, M., & Wei, F. (2026) · Plants · doi:10.3390/plants15192927
Metadata
- Authors
- Wang, T., Huang, J., Zhang, H., & Sun, Q.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Nanyang Technological University (SG)
- Singapore Management University (SG)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10757-10767
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035651653
- Cited by
- 291
- References
- 91
- Citations by year
- 2026: 18 · 2025: 35 · 2024: 58 · 2023: 65 · 2022: 52 · 2021: 53 · 2020: 10
- FWCI
- 13.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- 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)
- arXiv (Cornell University) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- unsupervised feature learning, visual commonsense reasoning, R-CNN, causal intervention, image captioning, visual question answering
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
