AttributeNet: Attribute Enhanced Vehicle Re-Identification

• URL: http://arxiv.org/abs/2102.03898v1
• Date: Sun, 7 Feb 2021 19:51:02 GMT
• Title: AttributeNet: Attribute Enhanced Vehicle Re-Identification
• Authors: Rodolfo Quispe and Cuiling Lan and Wenjun Zeng and Helio Pedrini
• Abstract summary: We introduce AttributeNet (ANet) that jointly extracts identity-relevant features and attribute features. We enable the interaction by distilling the ReID-helpful attribute feature and adding it into the general ReID feature to increase the discrimination power. We validate the effectiveness of our framework on three challenging datasets.
• Score: 70.89289512099242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vehicle Re-Identification (V-ReID) is a critical task that associates the same vehicle across images from different camera viewpoints. Many works explore attribute clues to enhance V-ReID; however, there is usually a lack of effective interaction between the attribute-related modules and final V-ReID objective. In this work, we propose a new method to efficiently explore discriminative information from vehicle attributes (e.g., color and type). We introduce AttributeNet (ANet) that jointly extracts identity-relevant features and attribute features. We enable the interaction by distilling the ReID-helpful attribute feature and adding it into the general ReID feature to increase the discrimination power. Moreover, we propose a constraint, named Amelioration Constraint (AC), which encourages the feature after adding attribute features onto the general ReID feature to be more discriminative than the original general ReID feature. We validate the effectiveness of our framework on three challenging datasets. Experimental results show that our method achieves state-of-the-art performance.

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