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

• URL: http://arxiv.org/abs/2004.06271v3
• Date: Fri, 17 Jul 2020 06:08:17 GMT
• Title: The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification
• Authors: Pirazh Khorramshahi, Neehar Peri, Jun-cheng Chen, Rama Chellappa
• Abstract summary: Self-supervised Attention for Vehicle Re-identification (SAVER) is a novel approach to effectively learn vehicle-specific discriminative features. We show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.
• Score: 75.3310894042132
• License: http://creativecommons.org/licenses/by/4.0/
• 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 VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.

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