Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2020

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.

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 33rd Conference on Computer Vision and Pattern Recognition, CVPR '20

Identifier

10.1109/CVPR42600.2020.01077

Publisher

IEEE

City or Country

Washington, United States

Additional URL

https://doi.org/10.1109/CVPR42600.2020.01077

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