Decoupled Contrastive Learning for Intra-Camera Supervised Person Re-identification

Hu, Shiteng; Zhang, Xin; Xie, Xiaohua

doi:10.1109/icpr56361.2022.9956299

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Metric Learning is typically based on some form of contrastive learning [ 24 ], either using contrastive loss [ 25 , 26 , 27 , 28 ] or, more frequently, triplet loss [ 29 , 30 , 31 , 32 ]. Both approaches aim to encourage similar examples to be close in the feature space and dissimilar examples to be separated.…”

Section: Introductionmentioning

confidence: 99%

A Generative Approach to Person Reidentification

Asperti,

Fiorilla,

Orsini

2024

Sensors

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Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.

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