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arXiv 2311.13435cs.CVcs.AI
PG-Video-LLaVA：像素定位大型视频-语言模型
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；University of Central Florida（中佛罗里达大学）；Linköping University（林雪平大学）
Shehan Munasinghe, Rusiru Thushara, Muhammad Maaz, Hanoona Abdul Rasheed, Salman Khan, Mubarak Shah, Fahad Khan
Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data. The recent approaches extending image-based LMMs to videos either lack the grounding capabilities (e.g., VideoChat, Video-ChatGPT, Video-LLaMA) or do not utilize the audio-signals for better video understanding (e.g., Video-ChatGPT). Addressing these gaps, we propose PG-Video-LLaVA, the first LMM with pixel-level grounding capability, integrating audio cues by transcribing them into text to enrich video-context understanding. Our framework uses an off-the-shelf tracker and a novel grounding module, enabling it to spatially localize objects in videos following user instructions. We evaluate PG-Video-LLaVA using video-based generative and question-answering benchmarks and introduce new benchmarks specifically designed to measure prompt-based object grounding performance in videos. Further, we propose the use of Vicuna over GPT-3.5, as utilized in Video-ChatGPT, for video-based conversation benchmarking, ensuring reproducibility of results which is a concern with the proprietary nature of GPT-3.5. Our framework builds on SoTA image-based LLaVA model and extends its advantages to the video domain, delivering promising gains on video-based conversation and grounding tasks. Project Page: https://github.com/mbzuai-oryx/Video-LLaVA
Visual Position Prompt for MLLM based Visual Grounding
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level
University of Central Florida（中佛罗里达大学）；University of Western Australia（西澳大学）；Amazon Web Services（亚马逊网络服务）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
