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

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Sep 17

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
1d ago

Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis

The paper evaluates 14 state‑of‑the‑art text‑to‑video retrieval models on three major datasets using a unified framework. It finds that short, clear, and simple captions—especially those describing single actions or color attributes—yield higher recall, while complex, multi‑step events or fine‑grained scene descriptions remain difficult for all models. Attention‑driven architectures better handle temporally dependent or multi‑step queries, whereas dual‑encoder and multimodal fusion models excel mainly on simpler captions, and cross‑dataset generalization improves with larger, more diverse caption sets but generative captions do not consistently boost accuracy.

By Maria-Eirini Pegia, Dimitrios Stefanopoulos, Bj\"orn {\TH}\'or J\'onsson, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
arXiv AI
Oct 9

Mid-Training Language Models on Raw Video

The paper investigates using raw, unlabeled video as a mid‑training phase for a pretrained language model. By encoding video frames into visual tokens and training the model to predict the next token, the authors mid‑train Qwen3‑1.7B on YT‑Temporal‑1B clips. The resulting model outperforms a non‑mid‑trained counterpart by 2.9 points on video benchmarks and 5.1 points on image benchmarks, while maintaining comparable text performance.

By Jaedong Hwang, Xiaoqian Shen, Ernie Chang, Changsheng Zhao, Chong Zhou, Saksham Suri, Qi Qian, Zechun Liu, Lemeng Wu, Qinsi Wang, Raghuraman Krishnamoorthi, Wei Wen
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 24

MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos

MultiVENT‑Raw is a new multilingual benchmark comprising nearly 120,000 raw videos—continuous footage from cell phones, hand‑held cameras, or CCTV—totaling over 5,300 hours. The dataset includes 130 events and 222 event‑centric queries, along with human‑annotated relevance judgments and extracted key facts for relevant videos. It supports two tasks: retrieving videos relevant to a query event and generating a coherent report summarizing event‑related videos for a target user, with baseline models showing these tasks remain challenging.

By Reno Kriz, David Etter, Alexander Martin, Cameron Carpenter, Debashish Chakraborty, Hannah Recknor, Reihaneh Iranmanesh, Matthew Maciejewski, Kenton Murray, Eugene Yang, Benjamin Van Durme, Aaron Steven White, Andrew Yates, William Walden