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jiakai

real-jiakai

AI & ML interests

LLM && Smart QA

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liked a model about 8 hours ago
ASLP-lab/DiffRhythm-base
liked a model about 8 hours ago
alibaba-pai/Wan2.1-Fun-1.3B-Control
reacted to tonywu71's post with šŸ”„ about 12 hours ago
ColPali: A new approach to efficient and intelligent document retrieval šŸš€ Our latest research paper, "ColPali: Efficient Document Retrieval with Vision Language Models," introduces a groundbreaking approach to large-scale visual document analysis. By leveraging Vision Language Models (VLMs), we have created a new framework for document retrieval that's both powerful and efficient. Key Insights: šŸ’” ColPali combines ColBERT's multi-vector strategy with VLMs' document understanding capabilities āš™ļø ColPali is based on PaliGemma-3B (SigLIP, Gemma-2B) + a linear projection layer and is trained to maximize the similarity between the document and the query embeddings šŸ“Š The Vision Document Retrieval benchmark (ViDoRe) is a challenging dataset that spans various industry topics and aims at matching real-life retrieval scenarios šŸ† ColPali outperforms existing models on all datasets in ViDoRe (average NDCG@5 of 81.3% vs 67.0% for the best baseline model) āš” ColPali is faster at document embedding compared to traditional PDF parser pipelines, making ColPali viable for industrial use šŸ” ColPali is highly interpretable thanks to patch-based similarity maps Dive deeper into ColPali and explore our resources: šŸ“‘ Full paper: arxiv.org/abs/2407.01449 šŸ› ļø Datasets, model weights, evaluation code, leaderboard, demos: huggingface.co/vidore Shoutout to my amazing co-authors Manuel Faysse (@manu) and Hugues Sibille (@HugSib). We are grateful for the invaluable feedback from Bilel Omrani, Gautier Viaud, Celine Hudelot, and Pierre Colombo. This work is sponsored by ILLUIN Technology. āœØ
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