VisNet: Efficient Person Re-Identification via Alpha-Divergence Loss, Feature Fusion and Dynamic Multi-Task Learning

TL;DR

VisNet is a computationally efficient person re-identification model that combines multi-scale feature fusion, semantic clustering, and dynamic loss balancing to achieve high accuracy with low resource consumption for real-world applications.

Contribution

The paper introduces VisNet, a novel re-identification model that integrates feature fusion, semantic clustering, and dynamic multi-task learning for improved efficiency and accuracy.

Findings

Achieves 87.05% Rank-1 accuracy on Market-1501

Has 32.41 million parameters and 4.601 GFLOPs

Suitable for real-time deployment in resource-constrained environments

Abstract

Person re-identification (ReID) is an extremely important area in both surveillance and mobile applications, requiring strong accuracy with minimal computational cost. State-of-the-art methods give good accuracy but with high computational budgets. To remedy this, this paper proposes VisNet, a computationally efficient and effective re-identification model suitable for real-world scenarios. It is the culmination of conceptual contributions, including feature fusion at multiple scales with automatic attention on each, semantic clustering with anatomical body partitioning, a dynamic weight averaging technique to balance classification semantic regularization, and the use of loss function FIDI for improved metric learning tasks. The multiple scales fuse ResNet50's stages 1 through 4 without the use of parallel paths, with semantic clustering introducing spatial constraints through the use of rule-based pseudo-labeling. VisNet achieves 87.05% Rank-1 and 77.65% mAP on the Market-1501 dataset, having 32.41M parameters and 4.601 GFLOPs, hence, proposing a practical approach for real-time deployment in surveillance and mobile applications where computational resources are limited.