2 papers
cs.CV2026
Revealing the Semantic Selection Gap in DINOv3 through Training-Free Few-Shot Segmentation
Hussni Mohd Zakir, Eric Tatt Wei Ho
Recent self-supervised Vision Transformers (ViTs), such as DINOv3, provide rich feature representations for dense vision tasks. This study investigates the intrinsic few-shot seman…
cs.CV2024
Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals
Hussni Mohd Zakir, Eric Tatt Wei Ho
The Segment-Anything Model (SAM) is a vision foundation model for segmentation with a prompt-driven framework. SAM generates class-agnostic masks based on user-specified instance-r…