1 citations · 1 across the 8 of their papers we have counts for
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STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation
Syed Ariff Syed Hesham, Yun Liu, Guolei Sun +4
Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense spatiotemporal tokens whose qu…
Evaluating SAM2 for Video Semantic Segmentation
Syed Hesham Syed Ariff, Yun Liu, Guolei Sun +4
The Segmentation Anything Model 2 (SAM2) has proven to be a powerful foundation model for promptable visual object segmentation in both images and videos, capable of storing object…
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects
Guohuan Xie, Syed Ariff Syed Hesham, Wenya Guo +4
Video Scene Parsing (VSP) studies dense video understanding, where every pixel in each frame must be segmented, each region must be named, and each object identity must remain cohe…
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
Zhaochong An, Guolei Sun, Yun Liu +4
Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods e…
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu +5
Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have sho…
Rethinking Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu +5
This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point dist…