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

• URL: http://arxiv.org/abs/2001.03152v2
• Date: Tue, 5 May 2020 23:20:53 GMT
• Title: Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
• Authors: Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, Deepti Ghadiyaram
• Abstract summary: Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. This work focuses on addressing such contextual biases to improve the robustness of the learnt feature representations.
• Score: 113.44471186752018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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