Automatic Detection of Intro and Credits in Video using CLIP and Multihead Attention

• URL: http://arxiv.org/abs/2504.09738v1
• Date: Sun, 13 Apr 2025 22:08:18 GMT
• Title: Automatic Detection of Intro and Credits in Video using CLIP and Multihead Attention
• Authors: Vasilii Korolkov, Andrey Yanchenko,
• Abstract summary: We introduce a deep learning-based approach that formulates the problem as a sequence-to-sequence classification task.<n>Our method extracts frames at a fixed rate of 1 FPS, encodes them using CLIP, and processes the resulting feature representations with a multihead attention model.<n>The system achieves an F1-score of 91.0%, Precision of 89.0%, and Recall of 97.0% on the test set, and is optimized for real-time inference.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting transitions between intro/credits and main content in videos is a crucial task for content segmentation, indexing, and recommendation systems. Manual annotation of such transitions is labor-intensive and error-prone, while heuristic-based methods often fail to generalize across diverse video styles. In this work, we introduce a deep learning-based approach that formulates the problem as a sequence-to-sequence classification task, where each second of a video is labeled as either "intro" or "film." Our method extracts frames at a fixed rate of 1 FPS, encodes them using CLIP (Contrastive Language-Image Pretraining), and processes the resulting feature representations with a multihead attention model incorporating learned positional encoding. The system achieves an F1-score of 91.0%, Precision of 89.0%, and Recall of 97.0% on the test set, and is optimized for real-time inference, achieving 11.5 FPS on CPU and 107 FPS on high-end GPUs. This approach has practical applications in automated content indexing, highlight detection, and video summarization. Future work will explore multimodal learning, incorporating audio features and subtitles to further enhance detection accuracy.

Related papers

• VaQuitA: Enhancing Alignment in LLM-Assisted Video Understanding [63.075626670943116]
  We introduce a cutting-edge framework, VaQuitA, designed to refine the synergy between video and textual information. At the data level, instead of sampling frames uniformly, we implement a sampling method guided by CLIP-score rankings. At the feature level, we integrate a trainable Video Perceiver alongside a Visual-Query Transformer.
  arXiv  Detail & Related papers  (2023-12-04T19:48:02Z)
• Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data [102.0069667710562]
  This paper presents Open-VCLIP++, a framework that adapts CLIP to a strong zero-shot video classifier. We demonstrate that training Open-VCLIP++ is tantamount to continual learning with zero historical data. Our approach is evaluated on three widely used action recognition datasets.
  arXiv  Detail & Related papers  (2023-10-08T04:46:43Z)
• Differentially Private Video Activity Recognition [79.36113764129092]
  We propose Multi-Clip DP-SGD, a novel framework for enforcing video-level differential privacy through clip-based classification models. Our approach achieves 81% accuracy with a privacy budget of epsilon=5 on UCF-101, marking a 76% improvement compared to a direct application of DP-SGD.
  arXiv  Detail & Related papers  (2023-06-27T18:47:09Z)
• Key Frame Extraction with Attention Based Deep Neural Networks [0.0]
  We propose a deep learning-based approach for detection using a deep auto-encoder model with an attention layer. The proposed method first extracts the features from the video frames using the encoder part of the autoencoder and applies segmentation using the k-means algorithm to group these features and similar frames together. The method was evaluated on the TVSUM clustering video dataset and achieved a classification accuracy of 0.77, indicating a higher success rate than many existing methods.
  arXiv  Detail & Related papers  (2023-06-21T15:09:37Z)
• Video alignment using unsupervised learning of local and global features [0.0]
  We introduce an unsupervised method for alignment that uses global and local features of the frames. In particular, we introduce effective features for each video frame by means of three machine vision tools: person detection, pose estimation, and VGG network. The main advantage of our approach is that no training is required, which makes it applicable for any new type of action without any need to collect training samples for it.
  arXiv  Detail & Related papers  (2023-04-13T22:20:54Z)
• InternVideo: General Video Foundation Models via Generative and Discriminative Learning [52.69422763715118]
  We present general video foundation models, InternVideo, for dynamic and complex video-level understanding tasks. InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives. InternVideo achieves state-of-the-art performance on 39 video datasets from extensive tasks including video action recognition/detection, video-language alignment, and open-world video applications.
  arXiv  Detail & Related papers  (2022-12-06T18:09:49Z)
• Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition [25.888314212797436]
  We propose a novel video frame sampler for few-shot action recognition. Task-specific spatial-temporal frame sampling is achieved via a temporal selector (TS) and a spatial amplifier (SA) Experiments show a significant boost on various benchmarks including long-term videos.
  arXiv  Detail & Related papers  (2022-07-20T09:04:12Z)
• ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency [62.38914747727636]
  We study self-supervised video representation learning, which is a challenging task due to 1) a lack of labels for explicit supervision and 2) unstructured and noisy visual information. Existing methods mainly use contrastive loss with video clips as the instances and learn visual representation by discriminating instances from each other. In this paper, we observe that the consistency between positive samples is the key to learn robust video representations.
  arXiv  Detail & Related papers  (2021-06-04T08:44:50Z)
• Self-supervised Video Representation Learning by Context and Motion Decoupling [45.510042484456854]
  A challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias. We develop a method that explicitly decouples motion supervision from context bias through a carefully designed pretext task. Experiments show that our approach improves the quality of the learned video representation over previous works.
  arXiv  Detail & Related papers  (2021-04-02T02:47:34Z)
• Unsupervised Learning of Video Representations via Dense Trajectory Clustering [86.45054867170795]
  This paper addresses the task of unsupervised learning of representations for action recognition in videos. We first propose to adapt two top performing objectives in this class - instance recognition and local aggregation. We observe promising performance, but qualitative analysis shows that the learned representations fail to capture motion patterns.
  arXiv  Detail & Related papers  (2020-06-28T22:23:03Z)

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.