Visual Commonsense R-CNN

• URL: http://arxiv.org/abs/2002.12204v3
• Date: Mon, 27 Apr 2020 04:29:49 GMT
• Title: Visual Commonsense R-CNN
• Authors: Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun
• Abstract summary: We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN) VC R-CNN serves as an improved visual region encoder for high-level tasks such as captioning and VQA. 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.
• Score: 102.5061122013483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Code and feature are available at https://github.com/Wangt-CN/VC-R-CNN.

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