arXiv AI By Andreas Hochlehnert, Marianna Nezhurina, Mehdi Cherti, Andrej Radonjic, Thadd\"aus Wiedemer, Christoph Schuhmann, Romain Beaumont, Wieland Brendel, Bernhard Sch\"olkopf, A. Sophia Koepke, Jenia Jitsev, Matthias Bethge

LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training

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arXiv AI
1d ago

ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search

ShotFinder introduces a new benchmark for open‑domain video shot retrieval, formalizing editing requirements as keyframe‑oriented shot descriptions and adding five controllable constraints—temporal order, color, visual style, audio, and resolution. The benchmark comprises 1,210 high‑quality YouTube samples across 20 themes, generated with large models and verified by humans. A three‑stage retrieval pipeline—query expansion via video imagination, candidate video retrieval, and description‑guided shot localization—shows a notable performance gap to humans, especially for color and visual style constraints.

By Tao Yu, Haopeng Jin, Hao Wang, Shenghua Chai, Yujia Yang, Junhao Gong, Jiaming Guo, Minghui Zhang, Xinlong Chen, Zhenghao Zhang, Yuxuan Zhou, Yufei Xiong, Shanbin Zhang, Jiabing Yang, YiFan Zhang, Hongzhu Yi, Xinming Wang, Cheng Zhong, Xiao Ma, Zhang Zhang, Yan Huang, Liang Wang
arXiv Machine Learning
Jun 16

MVEB: Massive Video Embedding Benchmark

arXiv:2606. 14958v1 Announce Type: cross Abstract: We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering.

By Adnan El Assadi, Roman Solomatin, Isaac Chung, Chenghao Xiao, Deep Shah, Manan Dey, Shriya Sudhakar, Zacharie Bugaud, Wissam Siblini, Ayush Sunil Munot, Yashwanth Devavarapu, Rakshitha Ireddi, Michelle Yang, M\'arton Kardos, Niklas Muennighoff, Kenneth Enevoldsen
arXiv Computer Vision
Sep 7

Intrinsic Temporal Adaptation of CLIP for Partially Relevant Video Retrieval

The paper introduces Intrinsic Temporal Adaptation (ITA) for Partially Relevant Video Retrieval (PRVR), a task that seeks untrimmed videos containing moments relevant to a text query. ITA employs a Backbone-Internal Temporal Adaptation that lets the final visual transformer layers attend to neighboring frames, creating temporally aware embeddings while keeping CLIP frozen. Additionally, an Affinity-Weighted Gradient Propagation technique softly aggregates top‑k frames based on text‑frame affinities to better handle the weakly supervised nature of PRVR, leading to state‑of‑the‑art performance and more accurate frame‑level evidence retrieval.

By Hyun Seok Seong, Woojin Jun, SuBeen Lee, Jae-Pil Heo
arXiv AI
Jul 20

LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

arXiv:2604. 10024v2 Announce Type: replace-cross Abstract: Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded.

By Alkesh Patel, Melis Ozyildirim, Ying-Chang Cheng, Ganesh Nagarajan