Vision-language models display large performance differences and clear limits in zero-shot country-level geolocalization from ground-view photos, with semantic cues helping coarse guesses but failing on fine details.
Llava-next: Improved reason- ing, ocr, and world knowledge
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OneClip-RAG enables MLLMs to handle long videos via one-shot clip retrieval and unified chunking-retrieval, delivering performance gains like matching GPT-5 level on MLVU with high efficiency on standard GPUs.
citing papers explorer
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Where Do Vision-Language Models Fail? World Scale Analysis for Image Geolocalization
Vision-language models display large performance differences and clear limits in zero-shot country-level geolocalization from ground-view photos, with semantic cues helping coarse guesses but failing on fine details.
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Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
OneClip-RAG enables MLLMs to handle long videos via one-shot clip retrieval and unified chunking-retrieval, delivering performance gains like matching GPT-5 level on MLVU with high efficiency on standard GPUs.