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- W4288402205 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." @default.
- W4288402205 created "2022-07-29" @default.
- W4288402205 creator A5024769212 @default.
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- W4288402205 date "2019-03-30" @default.
- W4288402205 modified "2023-09-23" @default.
- W4288402205 title "Person Re-identification with Bias-controlled Adversarial Training" @default.
- W4288402205 doi "https://doi.org/10.48550/arxiv.1904.00244" @default.
- W4288402205 hasPublicationYear "2019" @default.
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