Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method

• URL: http://arxiv.org/abs/2203.15778v1
• Date: Tue, 29 Mar 2022 17:43:01 GMT
• Title: Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
• Authors: Washington Ramos, Michel Silva, Edson Araujo, Victor Moura, Keller Oliveira, Leandro Soriano Marcolino, Erickson R. Nascimento
• Abstract summary: This paper presents a novel weakly-supervised methodology to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the input video to a target length. We also propose the Extended Visually-guided Document Attention Network (VDAN+), which can generate a highly discriminative embedding space.
• Score: 6.172652648945223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growth of videos in our digital age and the users' limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information. Despite the remarkable progress that summarization methods have made, most of them can only select a few frames or skims, creating visual gaps and breaking the video context. This paper presents a novel weakly-supervised methodology based on a reinforcement learning formulation to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the input video to a target length without creating gaps in the final video. We also propose the Extended Visually-guided Document Attention Network (VDAN+), which can generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in Precision, Recall, and F1 Score against the baselines while effectively controlling the video's output length. Visit https://www.verlab.dcc.ufmg.br/semantic-hyperlapse/tpami2022/ for code and extra results.

Related papers

• Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMs [56.040198387038025]
  We present a novel prompt-guided visual perception framework (abbreviated as Free Video-LLM) for efficient inference of training-free video LLMs. Our method effectively reduces the number of visual tokens while maintaining high performance across multiple video question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T12:35:12Z)
• Beyond the Frame: Single and mutilple video summarization method with user-defined length [4.424739166856966]
  Video summarizing is a difficult but significant work, with substantial potential for further research and development. In this paper, we combine a variety of NLP techniques (extractive and contect-based summarizers) with video processing techniques to convert a long video into a single relatively short video.
  arXiv  Detail & Related papers  (2023-12-23T04:32:07Z)
• 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)
• LOVECon: Text-driven Training-Free Long Video Editing with ControlNet [9.762680144118061]
  This paper aims to bridge the gap, establishing a simple and effective baseline for training-free diffusion model-based long video editing. We build the pipeline upon ControlNet, which excels at various image editing tasks based on text prompts. Our method manages to edit videos comprising hundreds of frames according to user requirements.
  arXiv  Detail & Related papers  (2023-10-15T02:39:25Z)
• Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators [70.17041424896507]
  Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. We propose a new task of zero-shot text-to-video generation using existing text-to-image synthesis methods. Our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data.
  arXiv  Detail & Related papers  (2023-03-23T17:01:59Z)
• Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos [39.42509966219001]
  This paper studies weakly supervised sequential video understanding where the accurate time-level text-video alignment is not provided. We use a transformer to aggregate frame-level features for video representation and use a pre-trained text encoder to encode the texts corresponding to each action and the whole video. Experiments on video sequence verification and text-to-video matching show that our method outperforms baselines by a large margin.
  arXiv  Detail & Related papers  (2023-03-22T08:13:25Z)
• HierVL: Learning Hierarchical Video-Language Embeddings [108.77600799637172]
  HierVL is a novel hierarchical video-language embedding that simultaneously accounts for both long-term and short-term associations. We introduce a hierarchical contrastive training objective that encourages text-visual alignment at both the clip level and video level. Our hierarchical scheme yields a clip representation that outperforms its single-level counterpart as well as a long-term video representation that achieves SotA.
  arXiv  Detail & Related papers  (2023-01-05T21:53:19Z)
• Deep Video Prior for Video Consistency and Propagation [58.250209011891904]
  We present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. We show that temporal consistency can be achieved by training a convolutional neural network on a video with Deep Video Prior.
  arXiv  Detail & Related papers  (2022-01-27T16:38:52Z)
• Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling [98.41300980759577]
  A canonical approach to video-and-language learning dictates a neural model to learn from offline-extracted dense video features. We propose a generic framework ClipBERT that enables affordable end-to-end learning for video-and-language tasks. Experiments on text-to-video retrieval and video question answering on six datasets demonstrate that ClipBERT outperforms existing methods.
  arXiv  Detail & Related papers  (2021-02-11T18:50:16Z)
• Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual Data [1.004766879203303]
  We present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos. Our approach can adaptively select frames that are not relevant to convey the information without creating gaps in the final video. We propose a novel network, called Visually-guided Document Attention Network (VDAN), able to generate a highly discriminative embedding space.
  arXiv  Detail & Related papers  (2020-03-31T14:07:45Z)

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.