Video Enriched Retrieval Augmented Generation Using Aligned Video Captions

• URL: http://arxiv.org/abs/2405.17706v1
• Date: Mon, 27 May 2024 23:39:17 GMT
• Title: Video Enriched Retrieval Augmented Generation Using Aligned Video Captions
• Authors: Kevin Dela Rosa,
• Abstract summary: "aligned visual captions" describe the visual and audio content of videos in a large corpus. Visual captions can be adapted to specific use cases by prompting the original foundational model / captioner for particular visual details or fine tuning.
• Score: 1.0878040851638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose the use of "aligned visual captions" as a mechanism for integrating information contained within videos into retrieval augmented generation (RAG) based chat assistant systems. These captions are able to describe the visual and audio content of videos in a large corpus while having the advantage of being in a textual format that is both easy to reason about & incorporate into large language model (LLM) prompts, but also typically require less multimedia content to be inserted into the multimodal LLM context window, where typical configurations can aggressively fill up the context window by sampling video frames from the source video. Furthermore, visual captions can be adapted to specific use cases by prompting the original foundational model / captioner for particular visual details or fine tuning. In hopes of helping advancing progress in this area, we curate a dataset and describe automatic evaluation procedures on common RAG tasks.

Related papers

• MLLM as Video Narrator: Mitigating Modality Imbalance in Video Moment Retrieval [53.417646562344906]
  Video Moment Retrieval (VMR) aims to localize a specific temporal segment within an untrimmed long video given a natural language query. Existing methods often suffer from inadequate training annotations, i.e., the sentence typically matches with a fraction of the prominent video content in the foreground with limited wording diversity. This intrinsic modality imbalance leaves a considerable portion of visual information remaining unaligned with text. In this work, we take an MLLM as a video narrator to generate plausible textual descriptions of the video, thereby mitigating the modality imbalance and boosting the temporal localization.
  arXiv  Detail & Related papers  (2024-06-25T18:39:43Z)
• Learning text-to-video retrieval from image captioning [59.81537951811595]
  We describe a protocol to study text-to-video retrieval training with unlabeled videos. We assume (i) no access to labels for any videos, and (ii) access to labeled images in the form of text. We show that automatically labeling video frames with image captioning allows text-to-video retrieval training.
  arXiv  Detail & Related papers  (2024-04-26T15:56:08Z)
• SOVC: Subject-Oriented Video Captioning [59.04029220586337]
  We propose a new video captioning task, Subject-Oriented Video Captioning (SOVC), which aims to allow users to specify the describing target via a bounding box. To support this task, we construct two subject-oriented video captioning datasets based on two widely used video captioning datasets.
  arXiv  Detail & Related papers  (2023-12-20T17:44:32Z)
• Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training [70.83385449872495]
  The correlation between the vision and text is essential for video moment retrieval (VMR) Existing methods rely on separate pre-training feature extractors for visual and textual understanding. We propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments.
  arXiv  Detail & Related papers  (2023-02-28T19:29:05Z)
• Controllable Video Captioning with an Exemplar Sentence [89.78812365216983]
  We propose a novel Syntax Modulated Caption Generator (SMCG) incorporated in an encoder-decoder-reconstructor architecture. SMCG takes video semantic representation as an input, and conditionally modulates the gates and cells of long short-term memory network. We conduct experiments by collecting auxiliary exemplar sentences for two public video captioning datasets.
  arXiv  Detail & Related papers  (2021-12-02T09:24:45Z)
• Open-book Video Captioning with Retrieve-Copy-Generate Network [42.374461018847114]
  In this paper, we convert traditional video captioning task into a new paradigm, ie, Open-book Video Captioning. We propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is constructed to retrieve sentences as hints from the training corpus effectively. Our framework coordinates the conventional retrieval-based methods with orthodox encoder-decoder methods, which can not only draw on the diverse expressions in the retrieved sentences but also generate natural and accurate content of the video.
  arXiv  Detail & Related papers  (2021-03-09T08:17:17Z)
• QuerYD: A video dataset with high-quality text and audio narrations [85.6468286746623]
  We introduce QuerYD, a new large-scale dataset for retrieval and event localisation in video. A unique feature of our dataset is the availability of two audio tracks for each video: the original audio, and a high-quality spoken description. The dataset is based on YouDescribe, a volunteer project that assists visually-impaired people by attaching voiced narrations to existing YouTube videos.
  arXiv  Detail & Related papers  (2020-11-22T17:33:44Z)
• Enriching Video Captions With Contextual Text [9.994985014558383]
  We propose an end-to-end sequence-to-sequence model which generates video captions based on visual input. We do not preprocess the text further, and let the model directly learn to attend over it.
  arXiv  Detail & Related papers  (2020-07-29T08:58:52Z)

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.