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Found 7 papers, 3 papers with code
Beyond Patches: Mining Interpretable Part-Prototypes for Explainable AI
Deep learning has provided considerable advancements for multimedia systems, yet the interpretability of deep models remains a challenge.
From Cross-Modal to Mixed-Modal Visible-Infrared Re-Identification
While current VI-ReID methods focus on cross-modality matching, real-world applications often involve mixed galleries containing both V and I images, where state-of-the-art methods show significant performance limitations due to large domain shifts and low discrimination across mixed modalities.
Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification
In particular, our method minimizes the cross-modal gap by identifying and aligning shared prototypes that capture key discriminative features across modalities, then uses multiple bridging steps based on this information to enhance the feature representation.
Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification
In this paper, the Adaptive Generation of Privileged Intermediate Information training approach is introduced to adapt and generate a virtual domain that bridges discriminant information between the V and I modalities.
Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data
For realistic evaluation of multimodal (and cross-modal) V-I person ReID models, we propose new challenging corrupted datasets for scenarios where V and I cameras are co-located (CL) and not co-located (NCL).
Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data
Several deep learning models have been proposed for visible-infrared (V-I) person ReID to recognize individuals from images captured using RGB and IR cameras.
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
% This paper introduces a novel approach for a creating intermediate virtual domain that acts as bridges between the two main domains (i. e., RGB and IR modalities) during training.
