The paper introduces BodyCam-VQA, an adaptive visual question answering framework designed to improve captioning of police body‑worn camera footage. By employing structured multimodal reasoning and probe question generation, the system extracts fine‑grained visual evidence that conventional captioning models miss. Experiments with various question generation models show that this VQA‑driven approach yields more reliable, objective, and detailed records of enforcement events.
By Karish Gupta, Matthew Alex, Alex Li, Yang Wu, Yun-Wei Chu, Kashif Munir, Xiaotian Zhou, Zhengping Ji, Xiaozhong Liu
The paper introduces an interdisciplinary framework that uses AI and machine learning to analyze police body‑worn camera footage from the Rochester Police Department. It combines image, audio, and natural language processing—including speaker separation, transcription, and large language models—to detect and classify interaction patterns such as respect, disrespect, escalation, and de‑escalation. A custom evaluation pipeline assesses transcription quality and behavior detection accuracy, aiming to support law‑enforcement review, training, and accountability.
By Anita Srbinovska, Angela Srbinovska, Vivek Senthil, Jonathan Bateman, Adrian Martin, John McCluskey, Ernest Fokou\'e
The paper introduces an audio-first triage method for budgeted vision‑language captioning of untrimmed egocentric video. By selecting windows for a vision‑language model based on lightweight audio features before any video decoding, the approach reduces costly model calls. It achieves 4.0–10.8 percentage point improvements in action coverage across call rates, cuts 9–20% of VLM calls at matched coverage on EPIC‑KITCHENS‑100, and outperforms uniform sampling and recent visual keyframe selectors on Ego4D.
By Masoud Jalayer, Changyi Li, Yu Xiao
arXiv:2609.06991v1 Announce Type: cross
Abstract: Recent text-to-audio-video (T2AV) models jointly generate video, speech, sound effects, and ambience from a single text prompt. This capability poses...
By Suah Choi, Tae-Young Lee, Gyeong-Moon Park
Video-HolmesV2 is a new benchmark that tests multimodal large language models on their ability to reason with spatio‑temporal audio‑visual evidence in long videos. It requires models to justify answers with precise evidence, uses a multi‑model cross‑verification pipeline and a spatio‑temporal evidence‑aware metric, and introduces an audio‑text guided token compression framework to reduce long‑context noise. In evaluations, even strong proprietary models score below 60% while the proposed approach outperforms comparable open‑source omni‑models.
By Zhaoyang Wei, Zipeng Wang, Yushe Cao, Chenhui Qiang, Shuaibing Cheng, Xuesong Yang, Sen Nie, Bowen Jiang, Wenchao Ding, Yanchao Hao, Zheng Wei, Xuehui Yu, Zhenjun Han
arXiv:2606. 05748v1 Announce Type: cross Abstract: Global-scale video moderation faces a dual challenge: the need for fine-grained multi-modal reasoning and the demand for interpretable outputs to support downstream enforcement.
By Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao
arXiv:2606. 30059v1 Announce Type: new Abstract: Industry-scale video and live-streaming moderation imposes requirements that are difficult to satisfy with generic pretrained public models or external APIs, including adaptation to platform-specific data distributions, policy-specific objectives, and product-level safety constraints.
By Shuchang Ye, Jinqiang Yu, Zhujun Xiao, Yajing Kong, Yist Y. Lin, Yang Ma, Jiaxi Liu, Xiaolei Xu, Zheng Yu
Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluations and inefficient c...
arXiv:2605. 00873v2 Announce Type: replace-cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods.
By Advait Tilak, Jiwon Choi, Nazifa Mouli, Wei Le
Existing video benchmarks evaluate action recognition on consumer videos, egocentric recordings, or simulated industrial environments. They do not test vision-language models under the visual and procedural conditions of real industrial CCTV, where workers appear as distant figures amid dust, steam, low light, glare, occlusion, and overlapping activities.
arXiv:2609.23416v1 Announce Type: cross
Abstract: Long-form audio performance is often summarized by context length and aggregate accuracy, obscuring how language, evidence, and task jointly shape di...
By Zeyu Yang, Xinyu Zhang, Zibo Bi, Pei Zhang, Xize Cheng, Jin Xu, Baosong Yang, Satoshi Nakamura
arXiv:2607. 25669v1 Announce Type: new Abstract: Emerging Omni-modal Large Language Models (OmniLLMs) enable unified understanding of text, audio, and video, but their long audio-video token sequences introduce substantial memory and inference costs.
By Haoyang Huang, Wenjie Huang, Tianqi Xu, Hongyaoxing Gu, Kang Tan, Yikai Fu, Yuhao Shen, Tianyu Liu, Baolin Zhang, Jun Zhang, Xinyi Hu, Jun Dai, Shuang Ge, Lei Chen, Yue Li, Mingchen Wang, Meng Zhang