5 accepted papers
Instance-level object segmentation across disparate egocentric and exocentric views is a fundamental challenge in visual understanding, critical for applications in embodied AI and remote collaboration. This task is exceptionally difficult due to severe changes in scale, perspective, and occlusion,
Collaborative perception shares information among different agents and helps solving problems that individual agents may face, e.g., occlusions and small sensing range. Prior methods usually separate the multi-agent fusion and multi-time fusion into two consecutive steps. In contrast, this paper pro…
This paper views the DETR's non-duplicate detection ability as a competition result among object queries. Around each object there are usually multiple queries within which only a single one can win the chance to become the final detection. Such a competition is hard: while some competing queries in…
End-to-end autonomous driving aims to build a fully differentiable system that takes raw sensor data as inputs and directly outputs the planned trajectory or control signals of the ego vehicle. State-of-the-art methods usually follow the `Teacher-Student' paradigm. The Teacher model uses privileged…
With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection. Many fusion approaches have been proposed, falling into two main categories: sparse-only or dense-only, differentiated by their feature representation within the fusion module. We analyze th…