Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification
Mahdi Alehdaghi, Arthur Josi, Rafael M. O. Cruz, Pourya Shamsolmoali, Éric Granger
Abstract
Visible-infrared person re-identification (V-I ReID) seeks to retrieve images of the same individual captured over a distributed network of RGB and IR sensors. Several V-I ReID approaches directly integrate the V and I modalities to represent images within a shared space. However, given the significant gap in the data distributions between V and I modalities, cross-modal V-I ReID remains challenging. A solution is to involve a privileged intermediate space to bridge between modalities, but in practice, such data is not available and requires selecting or creating effective mechanisms for informative intermediate domains. This paper introduces the Adaptive Generation of Privileged Intermediate Information (AGPI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) training approach to adapt and generate a virtual domain that bridges discriminative information between the V and I modalities. AGPI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> enhances the training of a deep V-I ReID backbone by generating and then leveraging bridging privileged information without modifying the model in the inference phase. This information captures shared discriminative attributes that are not easily ascertainable for the model within individual V or I modalities. Towards this goal, a non-linear generative module is trained with adversarial objectives, transforming V attributes into intermediate spaces that also contain I features. This domain exhibits less domain shift relative to the I domain compared to the V domain. Meanwhile, the embedding module within AGPI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> aims to extract discriminative modality-invariant features for both modalities by leveraging modality-free descriptors from generated images, making them a bridge between the main modalities. Experiments conducted on challenging V-I ReID datasets indicate that AGPI<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> consistently increases matching accuracy without additional computational resources during inference.