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.
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Backdoor intervention uses a do-operator to sever the dependency between a source object and context objects that are treated as potential confounders.
“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]
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- 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 ›
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