The paper introduces a new framework for unsupervised visible‑infrared person re‑identification that leverages modality‑unified prototypes. By contrasting with prototypes that unify both modalities, the method jointly optimizes similarity within and across modalities, improving modality invariance. A self‑distillation step refines instance‑prototype relationships using a steady teacher, resulting in a simple yet effective model validated on standard VI‑ReID benchmarks.
Visible-Infrared Person Re-Identification (VI-ReID) operates under a closed-world assumption, where queries and galleries are from heterogeneous modalities. However, in open-world scenarios, both sets are likely to contain homogeneous and heterogeneous modality images.
Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities, leading to suboptimal recognition performance. Current approaches ex...
The paper introduces FlexibleFusion, a method for infrared-visible object detection that adapts to both complete and missing-modality scenarios. It employs a Modality-Aware Experts Collaboration mechanism to selectively fuse cross-modal or intra-modal pathways, and a Residual Self-Paced Entropic Optimal Transport module to align heterogeneous feature distributions without heavy optimization. Experiments demonstrate consistent performance across various modality configurations.
The paper introduces DOD-SA, a framework for infrared-visible object detection that uses only single-modality annotations. It employs a Collaborative Teacher-Student Network with a single-modality branch and a dual-modality decoupled branch to transfer knowledge across modalities, and a Progressive and Self‑Tuning Training Strategy to refine pseudo‑labels. A Pseudo Label Assigner is also designed to align labels between modalities during training.
arXiv:2610.34305v1 Announce Type: new Abstract: Object re-identification requires embeddings that are robust to intra-class variation in viewpoint, illumination, and occlusion, yet preserve sharp sep...
arXiv:2606. 02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations.
arXiv:2610.32709v1 Announce Type: new Abstract: Text-enhanced visible-infrared person re-identification (VI-ReID) exploits textual descriptions to compensate for appearance cues that are weak or unav...
ODSS-Net introduces a framework for multi‑modal object re‑identification that separates shared structural information from modality‑specific cues. It employs a Shared Structural Feature Construction module to extract modality‑shared structure, an Orthogonal Specific Feature Learning module to isolate identity‑relevant local features, and a Sample Adaptive Mixture of Experts module to fuse these features based on shared structure. Experiments on three benchmarks confirm the effectiveness of this orthogonally decoupled representation learning approach.
The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations. While I2I ReID focuses on identity-level invariance across images of the same person, T2I ReID is driven by instance-specific textual descriptions tied to unique visual traits.
arXiv:2608.21099v1 Announce Type: cross Abstract: Multi-modal object detection is essential for robust scene understanding in challenging conditions, including low-light and adverse environments. Rec...
arXiv:2602. 17395v2 Announce Type: replace-cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes.
The paper investigates the conflict between image‑based (I2I) and text‑based (T2I) person re‑identification objectives when trained on a shared vision encoder. It shows that optimizing both simultaneously degrades performance because I2I contracts intra‑identity distances while T2I preserves them. A two‑stage pipeline—first training the encoder for I2I invariance and then freezing it while aligning the text encoder—improves T2I results, suggesting a path toward unified ReID systems.