The field of computer vision is rapidly advancing, with a focus on developing more robust and versatile models that can handle multiple modalities and scenarios. Recent research has emphasized the importance of unified frameworks that can seamlessly integrate different reference modalities and video modalities, enabling more effective and efficient tracking, detection, and recognition. Notably, innovative approaches have been proposed to address the challenges of thermal object detection, multi-view action recognition, and person re-identification across different modalities and viewpoints. These advancements have the potential to significantly improve the performance and applicability of computer vision systems in various real-world applications. Some noteworthy papers in this area include: UniSOT, which presents a unified framework for multi-modality single object tracking, demonstrating superior performance across various reference and video modalities. Contrast-Guided Cross-Modal Distillation achieves state-of-the-art performance in thermal object detection by introducing training-only objectives that sharpen instance-level decision boundaries and inject cross-modal semantic priors. MVAFormer proposes a novel transformer-based cooperation module for multi-view action recognition, effectively preserving spatial information and modeling relationships between multiple views. MTF-CVReID introduces a parameter-efficient framework for robust video person re-identification, achieving state-of-the-art performance on the AG-VPReID benchmark while maintaining real-time efficiency. Modality-Transition Representation Learning proposes a novel framework for visible-infrared person re-identification, aligning cross-modal features more effectively without requiring additional parameters. DINOv2 Driven Gait Representation Learning leverages rich visual priors to learn gait features complementary to appearance cues, facilitating robust sequence-level representations for cross-modal retrieval.