Pose Guided Gated Fusion for Person Re-identification

Person re-identification is an important yet challenging problem in visual recognition. Despite the recent advances with deep learning (DL) models for spatio-temporal and multi-modal fusion, re-identification approaches often fail to leverage the contextual information (e.g., pose and illumination) to dynamically select the most discriminant con-volutional filters (i.e., appearance features) for feature representation and inference. State-of-the-art techniques for gated fusion employ complex dedicated part- or attention-based architectures for late fusion, and do not incorporate pose and appearance information to train the backbone network. In this paper, a new DL model is proposed for pose-guided re-identification, comprised of a deep backbone, pose estimation, and gated fusion network. Given a query image of an individual, the backbone convolutional NN produces a feature embedding required for pair-wise matching with embeddings for reference images, where feature maps from the pose network and from mid-level CNN layers are combined by the gated fusion network to generate pose-guided gating. The proposed framework allows to dynamically activate the most discriminant CNN filters based on pose information in order to perform a finer grained recognition. Extensive experiments on three challenging benchmark datasets indicate that integrating the pose-guided gated fusion into the state-of-the-art re-identification backbone architecture allows to improve their recognition accuracy. Experimental results also support our intuition on the advantages of gating backbone appearance information using the pose feature maps at mid-level CNN layers.