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

Published in European Conference on Computer Vision, 2020

Recommended citation: Khorramshahi, Pirazh, Neehar Peri, Jun-cheng Chen, and Rama Chellappa. "The devil is in the details: Self-supervised attention for vehicle re-identification." In European Conference on Computer Vision, pp. 369-386. Springer, Cham, 2020.

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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