Weakly Supervised Person Re-Identification

TL;DR

This paper introduces a weakly supervised approach for person re-identification that leverages multi-instance multi-label learning to identify individuals across multiple camera views without requiring detailed annotations.

Contribution

It proposes a novel Cross-View MIML method integrated into deep neural networks to effectively handle weak supervision in person re-id tasks.

Findings

Effective in identifying persons with weak labels

Outperforms related methods on four datasets

Validates feasibility of weakly supervised re-id

Abstract

In the conventional person re-id setting, it is assumed that the labeled images are the person images within the bounding box for each individual; this labeling across multiple nonoverlapping camera views from raw video surveillance is costly and time-consuming. To overcome this difficulty, we consider weakly supervised person re-id modeling. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. Hence, for a video, there could be multiple video-level labels. We cast this weakly supervised person re-id challenge into a multi-instance multi-label learning (MIML) problem. In particular, we develop a Cross-View MIML (CV-MIML) method that is able to explore potential intraclass person images from all the camera views by incorporating the intra-bag alignment and the cross-view bag alignment. Finally, the CV-MIML method is embedded into an existing deep neural network for developing the Deep Cross-View MIML (Deep CV-MIML) model. We have performed extensive experiments to show the feasibility of the proposed weakly supervised setting and verify the effectiveness of our method compared to related methods on four weakly labeled datasets.