CodeMMR creates a unified embedding space for text, code, and images, outperforming baselines by 10 nDCG@10 points and boosting RAG code generation quality.
Learning transferable visual models from natural language supervision
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Synthetic facial images alone can train models for pediatric rare disease recognition to performance levels comparable to real-data baselines when generated at sufficient scale.
AgroCoT is a new Chain-of-Thought VQA benchmark with 4759 samples to evaluate reasoning capabilities of vision-language models in agriculture.
citing papers explorer
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CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval
CodeMMR creates a unified embedding space for text, code, and images, outperforming baselines by 10 nDCG@10 points and boosting RAG code generation quality.
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Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition
Synthetic facial images alone can train models for pediatric rare disease recognition to performance levels comparable to real-data baselines when generated at sufficient scale.
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AgroCoT: A Chain-of-Thought Benchmark for Evaluating Reasoning in Vision-Language Models for Agriculture
AgroCoT is a new Chain-of-Thought VQA benchmark with 4759 samples to evaluate reasoning capabilities of vision-language models in agriculture.