Core Insights - The article discusses the limitations of current video retrieval models, which are primarily optimized for coarse-grained text-video matching tasks, leading to biased training data and restricted model capabilities [1][6][7] - A new paradigm for video retrieval is proposed, shifting from specialized to universal models, with the introduction of the Universal Video Retrieval (UVR) concept and the Universal Video Retrieval Benchmark (UVRB) [2][12][16] - The GVE model, developed as part of this initiative, demonstrates superior generalization capabilities, outperforming existing models in zero-shot settings [3][4][26] Group 1: Current Challenges in Video Retrieval - Existing models excel on benchmarks like MSRVTT but struggle with complex real-world retrieval needs, such as multi-modal queries and fine-grained semantic understanding [6][7] - The training data for these models often comes from noisy labels, leading to a lack of robustness and generalization in complex scenarios [7][9] - The article highlights the need for a unified multi-modal representation framework in video retrieval, similar to advancements in image retrieval [8][9] Group 2: Introduction of UVR and UVRB - The UVR concept encompasses a comprehensive approach to video retrieval, integrating various tasks and domains to better reflect real-world scenarios [13][15] - The UVRB consists of 16 datasets covering multiple task types and domains, revealing the "偏科" (uneven performance) issue in existing models [17][18][28] - The benchmark aims to assess models across nine capabilities, emphasizing the need for a more holistic evaluation of video retrieval systems [17][18] Group 3: GVE Model Performance - The GVE model, available in 3B and 7B parameter versions, significantly outperforms 14 mainstream models, achieving an average Recall@1 score of 0.573 [26][27] - The GVE-3B model, with 3.8 billion parameters, surpasses larger models like Unite-7B, demonstrating that performance is driven by data quality and training strategies rather than sheer model size [27][31] - GVE-7B excels particularly in the "partially relevant video retrieval" task, showcasing its semantic discrimination capabilities [29][30] Group 4: Key Findings and Insights - The study reveals that traditional benchmarks like MSRVTT are misleading, with a low correlation to real-world performance, suggesting a need to incorporate "partially relevant retrieval" as a standard evaluation metric [38] - There is a significant disconnect between spatial and temporal understanding in current models, indicating a need for improved integration of these capabilities [39][40] - The architecture of models, such as CLIP and MLLM, influences their performance, with MLLM showing a more balanced learning approach across various tasks [41][42] Group 5: Future Directions - The research emphasizes the importance of developing a diagnostic, scalable, and reproducible framework for universal video retrieval, moving beyond mere performance metrics [48][49] - The combination of UVRB, high-quality synthetic data generation, and a structured training approach is expected to enhance model robustness and generalization [49][50] - The ultimate goal is to transition video retrieval from simple matching to a deeper understanding of content, necessitating new evaluation standards and richer training signals [48][49]