VLM-as-a-Judge for Person Re-Identification
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Updated
Nov 4, 2026 - Python
VLM-as-a-Judge for Person Re-Identification
Reproducible framework for document-centric VLM robustness and claim-faithfulness evaluation.
A specialized benchmarking framework for evaluating state-of-the-art Medical Vision-Language Models (VLMs) on clinical imaging datasets, optimized for resource-constrained environments and regional disease prevalence.
Procedural 3D scenes with exact ground truth (depth, masks, poses, orbit videos) for evaluating counting and relational reasoning in vision-language models
Official repo fpr research work: Evaluating Vision Encoder Quantization for Edge-Cloud VLMs: Where Evaluation Breaks When Moving from Development to Deployment
Is it really there? A deterministic two-view check of robot task outcomes. A VLM passed 10 of 18 printed decoys as real; geometry caught all 10.
Do frontier VLMs (Gemini/GPT/Claude) infer a robot’s goal from partial motion? Goal-inference benchmark for robot-motion legibility - MS thesis code.
Description: Domain-shift failure analysis of LLaVA-1.5-7B and InternVL2-8B across photos, charts, medical, and screenshots. 945 probes, 6 failure categories. Chart OCR: LLaVA 0% vs InternVL2 71%. Domain-dependent failure confirmed via chi-square (p<0.0001).
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