Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models

• URL: http://arxiv.org/abs/2307.04192v4
• Date: Sun, 31 Mar 2024 12:10:24 GMT
• Title: Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
• Authors: Wei Han, Hui Chen, Min-Yen Kan, Soujanya Poria,
• Abstract summary: Video understanding models usually randomly sample a set of frames or clips, regardless of internal correlations between their visual contents, nor their relevance to the problem. We propose two frame sampling strategies, namely the most domain frames (MDF) and most implied frames (MIF), to maximally preserve those frames that are most likely vital to the given questions.
• Score: 41.12711820047315
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Video question-answering is a fundamental task in the field of video understanding. Although current vision--language models (VLMs) equipped with Video Transformers have enabled temporal modeling and yielded superior results, they are at the cost of huge computational power and thus too expensive to deploy in real-time application scenarios. An economical workaround only samples a small portion of frames to represent the main content of that video and tune an image--text model on these sampled frames. Recent video understanding models usually randomly sample a set of frames or clips, regardless of internal correlations between their visual contents, nor their relevance to the problem. We argue that such kinds of aimless sampling may omit the key frames from which the correct answer can be deduced, and the situation gets worse when the sampling sparsity increases, which always happens as the video lengths increase. To mitigate this issue, we propose two frame sampling strategies, namely the most domain frames (MDF) and most implied frames (MIF), to maximally preserve those frames that are most likely vital to the given questions. MDF passively minimizes the risk of key frame omission in a bootstrap manner, while MIS actively searches key frames customized for each video--question pair with the assistance of auxiliary models. The experimental results on three public datasets from three advanced VLMs (CLIP, GIT and All-in-one) demonstrate that our proposed strategies can boost the performance for image-text pretrained models. The source codes pertaining to the method proposed in this paper are publicly available at https://github.com/declare-lab/sas-vqa.

Related papers

• VidCtx: Context-aware Video Question Answering with Image Models [15.1350316858766]
  We introduce VidCtx, a novel training-free VideoQA framework which integrates both visual information from input frames and textual descriptions of others frames. Experiments show that VidCtx achieves competitive performance among approaches that rely on open models.
  arXiv  Detail & Related papers  (2024-12-23T09:26:38Z)
• Multimodal Contextualized Support for Enhancing Video Retrieval System [0.0]
  We propose a system that integrates a novel retrieval pipeline that extracts multimodal data, and incorporate information from multiple frames within a video. The pipeline captures latent meanings, focusing on what can be inferred from the video clip, rather than just focusing on object detection in one single image.
  arXiv  Detail & Related papers  (2024-12-10T15:20:23Z)
• Weakly Supervised Gaussian Contrastive Grounding with Large Multimodal Models for Video Question Answering [11.244643114253773]
  Video Question (VideoQA) aims to answer natural language questions based on the information observed in videos. We propose a novel weakly supervised framework to enforce the LMMs to reason out the answers with question-critical moments as visual inputs.
  arXiv  Detail & Related papers  (2024-01-19T14:21:46Z)
• Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation [92.55296042611886]
  We propose a framework called "Reuse and Diffuse" dubbed $textitVidRD$ to produce more frames following the frames already generated by an LDM. We also propose a set of strategies for composing video-text data that involve diverse content from multiple existing datasets.
  arXiv  Detail & Related papers  (2023-09-07T08:12:58Z)
• Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation [93.18163456287164]
  This paper proposes a novel text-guided video-to-video translation framework to adapt image models to videos. Our framework achieves global style and local texture temporal consistency at a low cost.
  arXiv  Detail & Related papers  (2023-06-13T17:52:23Z)
• Efficient Cross-Modal Video Retrieval with Meta-Optimized Frames [39.03408879727955]
  Cross-modal video retrieval aims to retrieve semantically relevant videos given a text as a query. A common and simple solution is to uniformly sample a small number of frames from the video as input to ViT. To get free from this trade-off, this paper introduces an automatic video compression method based on a bilevel optimization program.
  arXiv  Detail & Related papers  (2022-10-16T05:35:00Z)
• MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization [61.69587867308656]
  We propose a multimodal hierarchical shot-aware convolutional network, denoted as MHSCNet, to enhance the frame-wise representation. Based on the learned shot-aware representations, MHSCNet can predict the frame-level importance score in the local and global view of the video.
  arXiv  Detail & Related papers  (2022-04-18T14:53:33Z)
• OCSampler: Compressing Videos to One Clip with Single-step Sampling [82.0417131211353]
  We propose a framework named OCSampler to explore a compact yet effective video representation with one short clip. Our basic motivation is that the efficient video recognition task lies in processing a whole sequence at once rather than picking up frames sequentially.
  arXiv  Detail & Related papers  (2022-01-12T09:50:38Z)
• Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA [96.10612095576333]
  We propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Our model is also comprised of dual-level attention (word/object and frame level), multi-head self-cross-integration for different sources (video and dense captions), and which pass more relevant information to gates. We evaluate our model on the challenging TVQA dataset, where each of our model components provides significant gains, and our overall model outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2020-05-13T16:35:27Z)

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.