3 accepted papers
Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended inference-time thinking. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can often degrade performanc
This work presents a simple yet effective workflow for automatically scaling instruction-following data to elicit pixel-level grounding capabilities of VLMs under complex instructions. In particular, we address five critical real-world challenges in text-instruction-based grounding: hallucinated ref…
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes and data distributions. When combined with the common practice of using a fixed threshold to declare a match, this gives r…