StreamChat: Chatting with Streaming Video

• URL: http://arxiv.org/abs/2412.08646v1
• Date: Wed, 11 Dec 2024 18:59:54 GMT
• Title: StreamChat: Chatting with Streaming Video
• Authors: Jihao Liu, Zhiding Yu, Shiyi Lan, Shihao Wang, Rongyao Fang, Jan Kautz, Hongsheng Li, Jose M. Alvare,
• Abstract summary: StreamChat is a novel approach that enhances the interaction capabilities of Large Multimodal Models with streaming video content. We introduce a flexible and efficient crossattention-based architecture to process dynamic streaming inputs. We construct a new dense instruction dataset to facilitate the training of streaming interaction models.
• Score: 85.02875830683637
• License:
• Abstract: This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction scenarios, existing methods rely solely on visual information available at the moment a question is posed, resulting in significant delays as the model remains unaware of subsequent changes in the streaming video. StreamChat addresses this limitation by innovatively updating the visual context at each decoding step, ensuring that the model utilizes up-to-date video content throughout the decoding process. Additionally, we introduce a flexible and efficient crossattention-based architecture to process dynamic streaming inputs while maintaining inference efficiency for streaming interactions. Furthermore, we construct a new dense instruction dataset to facilitate the training of streaming interaction models, complemented by a parallel 3D-RoPE mechanism that encodes the relative temporal information of visual and text tokens. Experimental results demonstrate that StreamChat achieves competitive performance on established image and video benchmarks and exhibits superior capabilities in streaming interaction scenarios compared to state-of-the-art video LMM.

Related papers

• Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge [57.01131456894516]
  Current video understanding models struggle with processing long video sequences, supporting multi-turn dialogues, and adapting to real-world dynamic scenarios. We propose StreamChat, a training-free framework for streaming video reasoning and conversational interaction. Our framework incorporates a parallel system scheduling strategy that enhances processing speed and reduces latency, ensuring robust performance in real-world applications.
  arXiv  Detail & Related papers  (2025-01-23T08:33:10Z)
• Streaming Detection of Queried Event Start [41.500121314019864]
  We propose a novel task for multimodal video understanding-Streaming Detection of Queried Event Start (SDQES) The goal of SDQES is to identify the beginning of a complex event as described by a natural language query, with high accuracy and low latency. We introduce a new benchmark based on the Ego4D dataset, as well as new task-specific metrics to study streaming multimodal detection of diverse events.
  arXiv  Detail & Related papers  (2024-12-04T18:58:27Z)
• Optical-Flow Guided Prompt Optimization for Coherent Video Generation [51.430833518070145]
  We propose a framework called MotionPrompt that guides the video generation process via optical flow. We optimize learnable token embeddings during reverse sampling steps by using gradients from a trained discriminator applied to random frame pairs. This approach allows our method to generate visually coherent video sequences that closely reflect natural motion dynamics, without compromising the fidelity of the generated content.
  arXiv  Detail & Related papers  (2024-11-23T12:26:52Z)
• Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models [64.2445487645478]
  Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio. We present Live2Diff, the first attempt at designing a video diffusion model with uni-directional temporal attention, specifically targeting live streaming video translation.
  arXiv  Detail & Related papers  (2024-07-11T17:34:51Z)
• VideoLLM-online: Online Video Large Language Model for Streaming Video [27.073238234038826]
  We propose a novel Learning-In-Video-Stream framework, which enables temporally aligned, long-context, and real-time conversation within a continuous video stream. Our framework supports streaming dialogue in a 5-minute video clip at over 10 FPS on an A100 GPU. It also showcases state-of-the-art performance on public offline video benchmarks, such as recognition, captioning, and forecasting.
  arXiv  Detail & Related papers  (2024-06-17T17:55:32Z)
• Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams [78.72965584414368]
  We present Flash-VStream, a video-language model that simulates the memory mechanism of human. Compared to existing models, Flash-VStream achieves significant reductions in latency inference and VRAM consumption. We propose VStream-QA, a novel question answering benchmark specifically designed for online video streaming understanding.
  arXiv  Detail & Related papers  (2024-06-12T11:07:55Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.