Your Interest, Your Summaries: Query-Focused Long Video Summarization

• URL: http://arxiv.org/abs/2410.14087v1
• Date: Thu, 17 Oct 2024 23:37:58 GMT
• Title: Your Interest, Your Summaries: Query-Focused Long Video Summarization
• Authors: Nirav Patel, Payal Prajapati, Maitrik Shah,
• Abstract summary: This paper introduces an approach for query-focused video summarization, aiming to align video summaries closely with user queries. We propose the Fully Convolutional Sequence Network with Attention (FCSNA-QFVS), a novel approach designed for this task.
• Score: 0.6041235048439966
• License:
• Abstract: Generating a concise and informative video summary from a long video is important, yet subjective due to varying scene importance. Users' ability to specify scene importance through text queries enhances the relevance of such summaries. This paper introduces an approach for query-focused video summarization, aiming to align video summaries closely with user queries. To this end, we propose the Fully Convolutional Sequence Network with Attention (FCSNA-QFVS), a novel approach designed for this task. Leveraging temporal convolutional and attention mechanisms, our model effectively extracts and highlights relevant content based on user-specified queries. Experimental validation on a benchmark dataset for query-focused video summarization demonstrates the effectiveness of our approach.

Related papers

• Personalized Video Summarization using Text-Based Queries and Conditional Modeling [3.4447129363520337]
  This thesis explores enhancing video summarization by integrating text-based queries and conditional modeling. Evaluation metrics such as accuracy and F1-score assess the quality of the generated summaries.
  arXiv  Detail & Related papers  (2024-08-27T02:43:40Z)
• GQE: Generalized Query Expansion for Enhanced Text-Video Retrieval [56.610806615527885]
  This paper introduces a novel data-centric approach, Generalized Query Expansion (GQE), to address the inherent information imbalance between text and video. By adaptively segmenting videos into short clips and employing zero-shot captioning, GQE enriches the training dataset with comprehensive scene descriptions. GQE achieves state-of-the-art performance on several benchmarks, including MSR-VTT, MSVD, LSMDC, and VATEX.
  arXiv  Detail & Related papers  (2024-08-14T01:24:09Z)
• Scaling Up Video Summarization Pretraining with Large Language Models [73.74662411006426]
  We introduce an automated and scalable pipeline for generating a large-scale video summarization dataset. We analyze the limitations of existing approaches and propose a new video summarization model that effectively addresses them. Our work also presents a new benchmark dataset that contains 1200 long videos each with high-quality summaries annotated by professionals.
  arXiv  Detail & Related papers  (2024-04-04T11:59:06Z)
• Query-based Video Summarization with Pseudo Label Supervision [19.229722872058055]
  Existing datasets for manually labelled query-based video summarization are costly and thus small. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels. Experimental results show that the proposed video summarization algorithm achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-04T22:28:17Z)
• VideoXum: Cross-modal Visual and Textural Summarization of Videos [54.0985975755278]
  We propose a new joint video and text summarization task. The goal is to generate both a shortened video clip along with the corresponding textual summary from a long video. The generated shortened video clip and text narratives should be semantically well aligned.
  arXiv  Detail & Related papers  (2023-03-21T17:51:23Z)
• CLIP-It! Language-Guided Video Summarization [96.69415453447166]
  This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization. We propose a language-guided multimodal transformer that learns to score frames in a video based on their importance relative to one another. Our model can be extended to the unsupervised setting by training without ground-truth supervision.
  arXiv  Detail & Related papers  (2021-07-01T17:59:27Z)
• Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos [63.94898634140878]
  Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. We propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. We evaluate the proposed method on three challenging public benchmarks: Ac-tivityNet Captions, TACoS, and Charades-STA.
  arXiv  Detail & Related papers  (2020-08-06T04:09:03Z)
• Convolutional Hierarchical Attention Network for Query-Focused Video Summarization [74.48782934264094]
  This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs. We propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot.
  arXiv  Detail & Related papers  (2020-01-31T04:30:14Z)

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.