Interaction-and-Aggregation Network for Person Re-identification

TL;DR

This paper introduces the Interaction-and-Aggregation (IA) network, enhancing CNNs for person re-identification by adaptively modeling spatial and channel feature interdependencies to better handle pose and scale variations.

Contribution

The novel IA network incorporates Spatial and Channel IA modules, allowing adaptive feature aggregation at any CNN depth, improving reID performance over existing methods.

Findings

Outperforms state-of-the-art on three benchmark datasets

Effectively models pose and scale variations

Enhances feature representation for small-scale cues

Abstract

Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in modeling the large variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, Interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly, Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for smallscale visual cues. Further, IA network can be constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets.