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The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification

2020-04-14 02:24:47
Pirazh Khorramshahi, Neehar Peri, Jun-cheng Chen, Rama Chellappa
arXiv_CV
         
Abstract

In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging vehicle re-id benchmarks including Veri-776, VehicleID, Vehicle-1M and Veri-Wild. SAVER demonstrates how proper regularization techniques significantly constrain the vehicle re-id task and help generate robust deep features.

Abstract (translated)

URL

https://arxiv.org/abs/2004.06271

PDF

https://arxiv.org/pdf/2004.06271.pdf

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