Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

The advent of large vision-language models (LVLMs) represents a remarkable advance in
the quest for artificial general intelligence. However, the models' effectiveness in both …

Despite significant progress in 3D point cloud segmentation, existing methods primarily
address specific tasks and depend on explicit instructions to identify targets, lacking the …

In referring segmentation, modeling the complicated constraints in the multimodal
information is one of the most challenging problems. As the information in a given language …

3D referring segmentation is an emerging and challenging vision-language task that aims to
segment the object described by a natural language expression in a point cloud scene. The …

Referring Video Object Segmentation (R-VOS) methods face challenges in maintaining
consistent object segmentation due to temporal context variability and the presence of other …

Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video
understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object …

We introduce VideoLISA, a video-based multimodal large language model designed to
tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the …

In this paper, we introduce Motion-Grounded Video Reasoning, a new motion
understanding task that requires generating visual answers (video segmentation masks) …

Despite the promising performance of current video segmentation models on existing
benchmarks, these models still struggle with complex scenes. In this paper, we introduce the …