Person Re-identification with Bias-controlled Adversarial Training

Sara Iodice; Krystian Mikolajczyk

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arXiv · 1904.00244

Person Re-identification with Bias-controlled Adversarial Training

Sara Iodice·Krystian Mikolajczyk

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Abstract

Inspired by the effectiveness of adversarial training in the area of Generative Adversarial Networks we present a new approach for learning feature representations in person re-identification. We investigate different types of bias that typically occur in re-ID scenarios, i.e., pose, body part and camera view, and propose a general approach to address them. We introduce an adversarial strategy for controlling bias, named Bias-controlled Adversarial framework (BCA), with two complementary branches to reduce or to enhance bias-related features. The results and comparison to the state of the art on different benchmarks show that our framework is an effective strategy for person re-identification. The performance improvements are in both full and partial views of persons.

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BibTeX RIS

Sara Iodice, Krystian Mikolajczyk. 2019-03-30. Person Re-identification with Bias-controlled Adversarial Training. https://arxiv.org/abs/1904.00244

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