Yuyang Tang

Yinchao Ma, Yuyang Tang, Wenfei Yang et al.

Single object tracking aims to localize target object with specific reference modalities (bounding box, natural language or both) in a sequence of specific video modalities (RGB, RGB+Depth, RGB+Thermal or RGB+Event.). Different reference modalities enable various human-machine interactions, and different video modalities are demanded in complex scenarios to enhance tracking robustness. Existing trackers are designed for single or several video modalities with single or several reference modalities, which leads to separate model designs and limits practical applications. Practically, a unified tracker is needed to handle various requirements. To the best of our knowledge, there is still no tracker that can perform tracking with these above reference modalities across these video modalities simultaneously. Thus, in this paper, we present a unified tracker, UniSOT, for different combinations of three reference modalities and four video modalities with uniform parameters. Extensive experimental results on 18 visual tracking, vision-language tracking and RGB+X tracking benchmarks demonstrate that UniSOT shows superior performance against modality-specific counterparts. Notably, UniSOT outperforms previous counterparts by over 3.0\% AUC on TNL2K across all three reference modalities and outperforms Un-Track by over 2.0\% main metric across all three RGB+X video modalities.

Wenping Jin, Yuyang Tang, Li Zhu et al.

A recent class of hyperspectral anomaly detection methods that can be trained once on background datasets and then universally deployed -- without per-scene retraining or parameter tuning -- has demonstrated remarkable efficiency and robustness. Building upon this paradigm, we focus on the integration of spectral and spatial cues and introduce a novel "Rebellious Student" framework for complementary feature learning. Unlike conventional teacher-student paradigms driven by imitation, our method intentionally trains the spatial branch to diverge from the spectral teacher, thereby learning complementary spatial patterns that the teacher fails to capture. A two-stage learning strategy is adopted: (1) a spectral enhancement network is first trained via reverse distillation to obtain robust background spectral representations; and (2) a spatial network -- the rebellious student -- is subsequently optimized using decorrelation losses that enforce feature orthogonality while maintaining reconstruction fidelity to avoid irrelevant noise. Once trained, the framework enhances both spectral and spatial background features, enabling parameter-free and training-free anomaly detection when paired with conventional detectors. Experiments on the HAD100 benchmark show substantial improvements over several established baselines with modest computational overhead, confirming the effectiveness of the proposed complementary learning paradigm. Our code is publicly available at https://github.com/xjpp2016/FERS.

Yinchao Ma, Yuyang Tang, Wenfei Yang et al.

Single object tracking aims to locate the target object in a video sequence according to the state specified by different modal references, including the initial bounding box (BBOX), natural language (NL), or both (NL+BBOX). Due to the gap between different modalities, most existing trackers are designed for single or partial of these reference settings and overspecialize on the specific modality. Differently, we present a unified tracker called UVLTrack, which can simultaneously handle all three reference settings (BBOX, NL, NL+BBOX) with the same parameters. The proposed UVLTrack enjoys several merits. First, we design a modality-unified feature extractor for joint visual and language feature learning and propose a multi-modal contrastive loss to align the visual and language features into a unified semantic space. Second, a modality-adaptive box head is proposed, which makes full use of the target reference to mine ever-changing scenario features dynamically from video contexts and distinguish the target in a contrastive way, enabling robust performance in different reference settings. Extensive experimental results demonstrate that UVLTrack achieves promising performance on seven visual tracking datasets, three vision-language tracking datasets, and three visual grounding datasets. Codes and models will be open-sourced at https://github.com/OpenSpaceAI/UVLTrack.