Recent advancements in MLLM-based long-form video understanding have mitigated inference-time computational cost and limited context lengths by selecting query-relevant frames. However, existing approaches predominantly rely on external proxy scorers and rigid heuristic rules, inevitably suffering from misalignment with the target MLLM's intrinsic evidence and failing to accommodate the non-uniform spatiotemporal information density.
arXiv:2609.15408v1 Announce Type: cross
Abstract: Long-video understanding remains challenging for multimodal large language models (MLLMs) because densely encoding long frame sequences is computatio...
By Hongchang Shi, Jinpeng Hu, Ao Wang, Wenzheng Zhou, Hui Ma, Feng Li, Zenglin Shi
The paper introduces AllocEmbed, an allocate‑then‑embed framework that reallocates a fixed visual‑input budget across more video frames to improve retrieval performance. A lightweight allocator uses low‑cost previews to assign frame‑wise resolutions before the embedding backbone, preserving detail where it most benefits retrieval while reducing visual cost elsewhere. Retrieval‑Driven Policy Optimization (RDPO) learns the allocator directly from retrieval feedback, and the method integrates with existing systems without modifying the embedding model.
By Song Jin, Zhongtao Jiang, Chenglei Shen, Huanxuan Liao, Haozhe Chi, Zhiwei Wang, Kun Xu, Yong Liu
arXiv:2607. 25266v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible.
By Ghazal Kaviani, Ghassan AlRegib
arXiv:2604.17422v2 Announce Type: replace
Abstract: Long video understanding remains a formidable challenge for Multimodal Large Language Models (MLLMs) due to the prohibitive cost of processing dens...
By Shaoguang Wang, Weiyu Guo, Ziyang Chen, Xuming Hu, Hui Xiong
arXiv:2609.10008v1 Announce Type: new
Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gal...
By Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar, Abdelrahman Mohamed Shaker, Rao Anwer
arXiv:2608. 03918v1 Announce Type: cross Abstract: Efficient long-video understanding requires vision--language models (VLMs) to reason over a small number of frames selected as sparse visual evidence.
By Ke Li, Jiayu Chen, Maoliang Li, Zihao Zheng, Hailong Zou, Hengyi Zhang, Xuanzhe Liu, Xiang Chen
arXiv:2608.05592v2 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) have made strong progress in video understanding, yet long videos remain difficult: the visual token budge...
By Ziling Huang, Shin'ichi Satoh
arXiv:2506. 01274v2 Announce Type: replace-cross Abstract: Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to video understanding remains constrained by suboptimal frame selection strategies, albeit with the rapid development of video-specialized LMMs.
By Hosu Lee, Junho Kim, Hyunjun Kim, Yong Man Ro
arXiv:2603. 18558v2 Announce Type: replace-cross Abstract: Long-form video question answering requires reasoning over extended temporal contexts, making frame selection a critical bottleneck for multi-modal large language models (MLLMs) bound by finite context windows.
By Dan Ben-Ami, Gabriele Serussi, Kobi Cohen, Chaim Baskin
arXiv:2608.29958v1 Announce Type: new
Abstract: Long videos contain far more visual content than Large Vision-Language Models (LVLMs) can process under a fixed visual-token budget, making frame selec...
By Shanqing Xu, Meng Luo, Mengchen Qian, Yuhui Gao, Siyue Peng, Xiaohan Zhong, Xiaojin Zhang, Zhongyu Wei, Wei Chen, Xiang Bai
arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.
By Linghao Meng, Qiankun Li, Junyuan Mao, Pujin Liao, Zhicheng He, Enbo Zhang, Kun Wang, Yang Liu, Huazhu Fu, Yueming Jin