RESEARCH

COMPUTER VISION

Don’t Judge an Object by Its Context: Learning to Overcome Contextual Bias

June 14, 2020

Abstract

Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model’s generalizability, especially when typical co-occurrence patterns are absent. This work focuses on addressing such contextual biases to improve the robustness of the learnt feature representations. Our goal is to accurately recognize a category in the absence of its context, without compromising on performance when it co-occurs with context. Our key idea is to decorrelate feature representations of a category from its co-occurring context. We achieve this by learning a feature subspace that explicitly represents categories occurring in the absence of context along side a joint feature subspace that represents both categories and context. Our very simple yet effective method is extensible to two multi-label tasks – object and attribute classification. On 4 challenging datasets, we demonstrate the effectiveness of our method in reducing contextual bias.

Download the Paper

AUTHORS

Written by

Krishna Kumar Singh

Dhruv Mahajan

Kristen Grauman

Yong Jae Lee

Matt Feiszli

Deepti Ghadiyaram

Publisher

Conference on Computer Vision and Pattern Recognition (CVPR)

Research Topics

Computer Vision

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