arXiv subjects

Yutong Yang

Publications and source records attributed to Yutong Yang.

2 recordsLinked to original sources

arXiv2026

TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking

Query denoising has become a standard training strategy for DETR-based detectors. Denoising queries, initialized by perturbing ground truths, share similarities with track queries in a typical DETR-based Multi-Object Tracking (MOT) method, warranting exploration of their potential synergy. However, query denoising in existing MOT methods is performed only within a single frame, preventing trackers from learning inter-frame temporal association from the denoising process. To address this issue, we propose TQD-Track, a Temporal Query Denoising (TQD) method tailored for MOT. In our method, denoising queries are initialized from ground truths in the previous frame and then propagated into the current frame in the same way as track queries, serving as additional independent data association candidates. These denoising queries carry temporal information and instance-specific feature representations, effectively emulating and augmenting track queries. Moreover, to simulate various real-world MOT challenges for robust tracking, we introduce several corresponding noise types to generate diverse denoising queries. We analyze the impact of our temporal query denoising for two tracking paradigms, tracking-by-attention and alternating detection and association, demonstrating its generalization. Extensive experiments on the nuScenes and Argoverse~2 datasets demonstrate that our approach consistently enhances different MOT baselines, requiring only modifications in the training process. Code and models are available at https://github.com/yutongy98/TQD-Track.

cs.CV↗

arXiv2026

Imagine Before Retrieval: Prospective Skill Retrieval for LLM Agents

Skill retrieval has recently emerged as a promising paradigm for identifying the desirable execution guidelines from the skill gallery, thus equipping large language model (LLM) agents with the procedural knowledge to accomplish the specified task. To this end, most existing methods customize the retrieval model or reconfigure the retrieval pipeline to prioritize skills that are most semantically relevant to the task query. However, we empirically reveal that task queries and skills are naturally formulated from different perspectives, namely, objective-oriented and procedural-oriented, leading to an under-explored problem termed Query--Skill Misalignment (QSM). Clearly, it is daunting and even impossible to associate the desirable skills in the context of QSM, thus hindering the agent from correctly executing the task. As a remedy, inspired by human prospective cognition, we propose SkillDreamer, a novel framework to alleviate the negative impact of QSM problem. In brief, SkillDreamer first infers the capabilities necessary for task execution, then imagines how to realize these capabilities by generating pseudo skills, and finally leverages such prospective information to bridge the gap between objective-oriented task queries and execution-oriented skills. Extensive experiments on SkillRet and SkillUsage not only verify the effectiveness of SkillDreamer in both skill retrieval and end-to-end task execution, but also demonstrate its generalizability across diverse retrieval models and pipelines. The code will be released upon acceptance.

cs.IR↗