Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2012.06843v1
• Date: Sat, 12 Dec 2020 15:39:11 GMT
• Title: Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification
• Authors: Can Zhang, Hong Liu, Wei Guo, Mang Ye
• Abstract summary: Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global. Cross-modality correlations can thus be efficiently explored on salient features for distinctive modality-invariant feature learning.
• Score: 35.55895776505113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the surveillance system under poor light conditions. Facing great challenges in complex variances including conventional single-modality and additional inter-modality discrepancies, most of the existing RGB-IR Re-ID methods propose to impose constraints in image level, feature level or a hybrid of both. Despite the better performance of hybrid constraints, they are usually implemented with heavy network architecture. As a matter of fact, previous efforts contribute more as pioneering works in new cross-modal Re-ID area while leaving large space for improvement. This can be mainly attributed to: (1) lack of abundant person image pairs from different modalities for training, and (2) scarcity of salient modality-invariant features especially on coarse representations for effective matching. To address these issues, a novel Multi-Scale Part-Aware Cascading framework (MSPAC) is formulated by aggregating multi-scale fine-grained features from part to global in a cascading manner, which results in a unified representation containing rich and enhanced semantic features. Furthermore, a marginal exponential centre (MeCen) loss is introduced to jointly eliminate mixed variances from intra- and inter-modal examples. Cross-modality correlations can thus be efficiently explored on salient features for distinctive modality-invariant feature learning. Extensive experiments are conducted to demonstrate that the proposed method outperforms all the state-of-the-art by a large margin.

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