1 citations · 1 across the 3 of their papers we have counts for
5 papers
LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection
Shangye Song, Tianzhi Zhu, Syed Ariff Syed Hesham +2
Camouflaged object detection (COD) aims to segment objects that exhibit high visual similarity to their surroundings, which reduces foreground-background discriminability and weake…
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…
Exploiting Temporal State Space Sharing for Video Semantic Segmentation
Syed Ariff Syed Hesham, Yun Liu, Guolei Sun +5
Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes. Traditional methods often segment videos frame-by-frame or in a short tempor…