Megl: multi-experts guided learning network for single camera training person re-identification

The time-saving single-camera training(SCT) person reidentification aims to learn camera-invariant information without cross-camera pedestrian annotations. To address this challenging task, we propose a novel approach called Multi-Experts Guided Learning Network (MEGL-Net) for SCT-ReID that can obtain features not influenced by camera views at the global and local levels under the guidance of multi-camera experts. Firstly, to obtain camera-invariant features, an adaptive feature integration module (AFI) is introduced to adaptively integrate expert-guided features from different camera branches. Then, the proposed camera-local interactive module (CLI) facilitates interaction between the local branch and the camera experts branch for automatically extracting discriminative, domain-invariant features at a fine-grained level. Finally, our framework aggregates expert-guided features with global features and enhanced local features in the testing stage for pedestrian retrieval. Under the Market-SCT and Duke-SCT datasets, experimental results demonstrate that our approach significantly improves ReID performance and outperforms existing state-of-the-art (SOTA) methods.