6 accepted papers
Reinforcement Learning (RL) enables robust and adaptive locomotion in legged and wheeled-legged robots. A common approach is the Teacher-Student (TS) paradigm, in which a teacher policy with privileged information supervises a proprioceptive student. While the TS paradigm has proven effective on leg…
Existing multimodal large language models (MLLMs) face significant challenges in Referring Video Object Segmentation(RVOS). We identify three critical challenges: (C1) insufficient quantitative representation of textual numerical data, (C2) repetitive and degraded response templates for spatiotempor…
In unknown environments, navigating a robot by a given image to a specific location or instance is critical and challenging. The existing end-to-end approaches require simultaneous implicit learning of multiple subtasks, and modular approaches depend on metric information. Both approaches face high
Timestamp supervised temporal action segmentation (TSTAS) is more cost-effective than fully supervised counterparts. However, previous approaches suffer from severe label bias due to over-reliance on sparse timestamp annotations, resulting in unsatisfactory performance. In this paper, we propose the…
Person search is a widely-concerned computer vision task that aims to jointly solve the problems of pedestrian detection and person re-identification in panoramic scenes. However, the pedestrian detection focuses on the consistency of pedestrians, while the person re-identification attempts to extra…
Temporal Action Segmentation (TAS) has achieved great success in many fields such as exercise rehabilitation, movie editing, etc. Currently, task-driven TAS is a central topic in human action analysis. However, motion-centered TAS, as an important topic, is little researched due to unavailable datas…