ColPali: Document Retrieval with Vision-Language Models only (with Manuel Faysse)

Neural Search Talks — Zeta Alpha

In this episode of Neural Search Talks, we're chatting with Manuel Faysse, a 2nd year PhD student from CentraleSupélec & Illuin Technology, who is the first author of the paper "ColPali: Efficient Document Retrieval with Vision Language Models". ColPali is making waves in the IR community as a simple but effective new take on embedding documents using their image patches and the late-interaction paradigm popularized by ColBERT. Tune in to learn how Manu conceptualized ColPali, his methodology for tackling new research ideas, and why this new approach outperforms all classic multimodal embedding models. A must-watch episode! Timestamps: 0:00 Introduction with Jakub & Manu 4:09 The "Aha!" moment that led to ColPali 7:06 Challenges that had to be solved 9:16 The main idea behind ColPali 13:20 How ColPali simplifies the IR pipeline 15:54 The ViDoRe benchmark 18:23 Why ColPali is superior to CLIP-based retrievers 20:41 The training setup used for ColPali 24:00 Optimizations to make ColPali more efficient 29:00 How ColPali could work with text-only datasets 31:21 Outro: The next steps for this line of research