3 papers
cs.CL2026
What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness
Raphaël Sarfati, Pratyush Ranjan Tiwari, Siddharth Boppana +3
Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a…
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…