Methods › General › Self-Supervised Learning › VC R-CNN
Visual Commonsense Region-based Convolutional Neural Network
VC R-CNN
Introduced by Tan Wang et al. in Visual Commonsense R-CNN
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
VC R-CNN is an unsupervised feature representation learning method, which uses Region-based Convolutional Neural Network (R-CNN) as the visual backbone, and the causal intervention as the training objective. 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 the chair is likely to exist if table is observed.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
Visual Commonsense R-CNN 27 Feb 2020 · 1 repository · arXiv:2002.12204Syntology ran 1 of 4 samples · 3 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections