arXiv AI August 5, 2026 By Ce Chen, Yi Ren, Yuanming Li, Viktor Goriachko, Zhenhui Ye, Zujin Guo, Zhibin Hong, Mingming Gong

TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions

Read the original on arXiv AI →

arXiv:2604. 27975v2 Announce Type: replace-cross Abstract: Traditional Shot Boundary Detection (SBD) inherently struggles with complex transitions by formulating the task around isolated cut points, frequently yielding corrupted video shots.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 12

Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation

arXiv:2608. 10932v1 Announce Type: cross Abstract: Understanding camera motion is fundamental to video perception, with applications in spatial intelligence and controllable video generation.

By Dazhao Du, Shiyan Du, Jian Liu, Yongjian Yu, Bohai Gu, Tao Han, Hualuo Liu, Eric Liu, Yujia Zhang, Xi Chen, Song Guo

llms diffusion efficiency multimodal benchmarks

arXiv AI

Jul 3

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

arXiv:2607. 02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG).

By Rintaro Otsubo, Ryo Fujii, Reina Ishikawa, Taiki Kanaya, Kanta Sawafuji, Hiroki Kajita, Shigeki Sakai, Hideo Saito, Ryo Hachiuma

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 8

MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models

arXiv:2606. 06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding.

By Yifan Xu, Chao Zhang, Ruifei Ma, Fei Gao, Zhifei Yang, Jiaxing Qi, Zhipeng Chen

llms agents diffusion multimodal benchmarks safety

arXiv AI

Jul 16

ScanFocus: A Coarse-to-Fine Framework for Spatio-Temporal Video Grounding

arXiv:2607. 13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural language expression.

By Kai Chen, Ming Dai, Wenxuan Cheng, Wankou Yang

multimodal benchmarks

arXiv AI

Jul 15

The TIME Machine: On The Power of Motion for Efficient Perception

arXiv:2605. 23045v2 Announce Type: replace-cross Abstract: Video representation learning has seen tremendous progress in recent years.

By Mantas Skackauskas, Xinyue Hao, Laura Sevilla-Lara

rag benchmarks

arXiv Computer Vision

Aug 25

Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos

arXiv:2501.04001v4 Announce Type: replace Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...

By Haobo Yuan, Xiangtai Li, Tao Zhang, Yueyi Sun, Zilong Huang, Shilin Xu, Shunping Ji, Yunhai Tong, Lu Qi, Jiashi Feng, Ming-Hsuan Yang

llms computer-vision fine-tuning multimodal benchmarks

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Temporally Grounded Compositional Camera Motion Understanding via Geometric Knowledge Distillation

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

MotionEnhancer: Leveraging Video Diffusion for Motion-Enhanced Vision-Language Models

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Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos