4 accepted papers
In warehouse environments, corridor conflicts often lead to traffic congestion, which result in many Multi-Agent Path Finding (MAPF) algorithms failing to find solutions within a reasonable time limit. Previous works have studied using corridor reasoning techniques or incorporating guidance, such as…
Achieving robust vision-based humanoid locomotion remains challenging due to two fundamental issues: the sim-toreal gap introduces significant perception noise that degrades performance on fine-grained tasks, and training a unified policy across diverse terrains is hindered by conflicting learning o…
Recently, Contrastive Language-Image Pre-training (CLIP) has shown promising performance in domain-specific data (e.g., biology), and has attracted increasing research attention. Existing works generally focus on collecting extensive domain-specific data and directly tuning the original CLIP models.…
Deep superpixel algorithms have made remarkable strides by substituting hand-crafted features with learnable ones. Nevertheless, we observe that existing deep superpixel methods, serving as mid-level representation operations, remain sensitive to the statistical properties (e.g., color distribution,…