Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding

• URL: http://arxiv.org/abs/2409.19532v1
• Date: Sun, 29 Sep 2024 03:33:35 GMT
• Title: Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding
• Authors: Xiao Wang, Jianlong Wu, Zijia Lin, Fuzheng Zhang, Di Zhang, Liqiang Nie,
• Abstract summary: This study quantitatively reveals an "impossible trinity" among data quantity, diversity, and quality in pre-training datasets. Recent efforts seek to refine large-scale, diverse ASR datasets compromised by low quality through synthetic annotations. We introduce the Video DataFlywheel framework, which iteratively refines video annotations with improved noise control methods.
• Score: 61.89781979702939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, video-language understanding has achieved great success through large-scale pre-training. However, data scarcity remains a prevailing challenge. This study quantitatively reveals an "impossible trinity" among data quantity, diversity, and quality in pre-training datasets. Recent efforts seek to refine large-scale, diverse ASR datasets compromised by low quality through synthetic annotations. These methods successfully leverage useful information in multimodal video content (frames, tags, ASR transcripts, etc.) to refine the original annotations. Nevertheless, they struggle to mitigate noise within synthetic annotations and lack scalability as the dataset size expands. To address these issues, we introduce the Video DataFlywheel framework, which iteratively refines video annotations with improved noise control methods. For iterative refinement, we first leverage a video-language model to generate synthetic annotations, resulting in a refined dataset. Then, we pre-train on it and fine-tune on human refinement examples for a stronger model. These processes are repeated for continuous improvement. For noise control, we present AdaTaiLr, a novel noise control method that requires weaker assumptions on noise distribution, thereby proving more effective in large datasets with theoretical guarantees. The combination of iterative refinement and AdaTaiLr can achieve better scalability in video-language understanding. Extensive experiments show that our framework outperforms existing data refinement baselines, delivering a 3% performance boost and improving dataset quality with minimal diversity loss. Furthermore, our refined dataset facilitates significant improvements in various video-language understanding tasks, including video question answering and text-video retrieval.

Related papers

• Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content [35.02160595617654]
  temporal splitting, detailed captions, and video quality filtering are three key factors that determine dataset quality. We introduce Koala-36M, a large-scale, high-quality video dataset featuring accurate temporal splitting, detailed captions, and superior video quality.
  arXiv  Detail & Related papers  (2024-10-10T17:57:49Z)
• 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)
• Video Summarization Based on Video-text Modelling [0.0]
  We propose a multimodal self-supervised learning framework to obtain semantic representations of videos. We also introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. An objective evaluation framework is proposed to measure the quality of video summaries based on video classification.
  arXiv  Detail & Related papers  (2022-01-07T15:21:46Z)
• Investigating Tradeoffs in Real-World Video Super-Resolution [90.81396836308085]
  Real-world video super-resolution (VSR) models are often trained with diverse degradations to improve generalizability. To alleviate the first tradeoff, we propose a degradation scheme that reduces up to 40% of training time without sacrificing performance. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences.
  arXiv  Detail & Related papers  (2021-11-24T18:58:21Z)
• Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding [69.40915115518523]
  Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Various data augmentation approaches have been proposed to synthesize training data in low-resource target languages. In this paper we focus on mitigating noise in augmented data.
  arXiv  Detail & Related papers  (2021-09-03T15:44:15Z)
• Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning [62.47593143542552]
  We describe a subset optimization approach for automatic dataset curation. We demonstrate that our approach finds videos with high audio-visual correspondence and show that self-supervised models trained on our data, despite being automatically constructed, achieve similar downstream performances to existing video datasets with similar scales.
  arXiv  Detail & Related papers  (2021-01-26T14:27:47Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)

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.