ShareGPT4Video: Improving Video Understanding and Generation with Better Captions

• URL: http://arxiv.org/abs/2406.04325v1
• Date: Thu, 6 Jun 2024 17:58:54 GMT
• Title: ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
• Authors: Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Bin Lin, Zhenyu Tang, Li Yuan, Yu Qiao, Dahua Lin, Feng Zhao, Jiaqi Wang,
• Abstract summary: We present the ShareGPT4Video series, aiming to facilitate the video understanding of large video-language models (LVLMs) and the video generation of text-to-video models (T2VMs) via dense and precise captions. The series comprises: ShareGPT4Video, 40K GPT4V annotated dense captions of videos with various lengths and sources, developed through carefully designed data filtering and annotating strategy. We further develop ShareCaptioner-Video, a superior captioner capable of efficiently generating high-quality captions for arbitrary videos.
• Score: 93.29360532845062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the ShareGPT4Video series, aiming to facilitate the video understanding of large video-language models (LVLMs) and the video generation of text-to-video models (T2VMs) via dense and precise captions. The series comprises: 1) ShareGPT4Video, 40K GPT4V annotated dense captions of videos with various lengths and sources, developed through carefully designed data filtering and annotating strategy. 2) ShareCaptioner-Video, an efficient and capable captioning model for arbitrary videos, with 4.8M high-quality aesthetic videos annotated by it. 3) ShareGPT4Video-8B, a simple yet superb LVLM that reached SOTA performance on three advancing video benchmarks. To achieve this, taking aside the non-scalable costly human annotators, we find using GPT4V to caption video with a naive multi-frame or frame-concatenation input strategy leads to less detailed and sometimes temporal-confused results. We argue the challenge of designing a high-quality video captioning strategy lies in three aspects: 1) Inter-frame precise temporal change understanding. 2) Intra-frame detailed content description. 3) Frame-number scalability for arbitrary-length videos. To this end, we meticulously designed a differential video captioning strategy, which is stable, scalable, and efficient for generating captions for videos with arbitrary resolution, aspect ratios, and length. Based on it, we construct ShareGPT4Video, which contains 40K high-quality videos spanning a wide range of categories, and the resulting captions encompass rich world knowledge, object attributes, camera movements, and crucially, detailed and precise temporal descriptions of events. Based on ShareGPT4Video, we further develop ShareCaptioner-Video, a superior captioner capable of efficiently generating high-quality captions for arbitrary videos...

Related papers

• VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding [15.959757105308238]
  Video LMMs rely on either image or video encoders to process visual inputs, each of which has its own limitations. We introduce VideoGPT+, which combines the complementary benefits of the image encoder (for detailed spatial understanding) and the video encoder (for global temporal context modeling) Our architecture showcases improved performance across multiple video benchmarks, including VCGBench, MVBench and Zero-shot question-answering.
  arXiv  Detail & Related papers  (2024-06-13T17:59:59Z)
• Vript: A Video Is Worth Thousands of Words [54.815686588378156]
  Vript is an annotated corpus of 12K high-resolution videos with detailed, dense, and script-like captions. Each clip has a caption of 145 words, which is over 10x longer than most video-text datasets. Vript is a powerful model capable of end-to-end generation of dense and detailed captions for long videos.
  arXiv  Detail & Related papers  (2024-06-10T06:17:55Z)
• I4VGen: Image as Stepping Stone for Text-to-Video Generation [22.3850273729521]
  I4VGen is a training-free and plug-and-play video diffusion inference framework. It enhances text-to-video generation by leveraging robust image techniques. I4VGen produces videos with higher visual realism and textual fidelity.
  arXiv  Detail & Related papers  (2024-06-04T11:48:44Z)
• InternVideo2: Scaling Foundation Models for Multimodal Video Understanding [51.129913789991924]
  InternVideo2 is a new family of video foundation models (FM) that achieve state-of-the-art results in video recognition, video-speech tasks, and video-centric tasks. Our core design is a progressive training approach that unifies the masked video modeling, cross contrastive learning, and prediction token, scaling up to 6B video size.
  arXiv  Detail & Related papers  (2024-03-22T17:57:42Z)
• VideoPrism: A Foundational Visual Encoder for Video Understanding [90.01845485201746]
  VideoPrism is a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips with noisy parallel text. We extensively test VideoPrism on four broad groups of video understanding tasks, from web video question answering to CV for science, achieving state-of-the-art performance on 31 out of 33 video understanding benchmarks.
  arXiv  Detail & Related papers  (2024-02-20T18:29:49Z)
• Subject-Oriented Video Captioning [64.08594243670296]
  We propose a new video captioning task, subject-oriented video captioning, which allows users to specify the describing target via a bounding box. We construct two subject-oriented video captioning datasets based on two widely used video captioning datasets: MSVD and MSRVTT. As the first attempt, we evaluate four state-of-the-art general video captioning models, and have observed a large performance drop.
  arXiv  Detail & Related papers  (2023-12-20T17:44:32Z)
• Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval? [131.300931102986]
  In real-world scenarios, online videos are often accompanied by relevant text information such as titles, tags, and even subtitles. We propose a novel approach to text-video retrieval, where we directly generate associated captions from videos using zero-shot video captioning. We conduct comprehensive ablation studies to demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-12-31T11:50:32Z)

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.