A Large-Scale Study on Video Action Dataset Condensation
Dataset condensation has made significant progress in the image domain. Unlike images, videos possess an additional temporal dimension, which harbors considerable redundant information, making condensation even more crucial. However, video dataset condensation still remains an underexplored area. We aim to bridge this gap by providing a large-scale empirical study with systematic design and fair comparison. Specifically, our work delves into three key aspects to provide valuable empirical insights: (1) temporal processing of video data, (2) establishing a comprehensive evaluation protocol for video dataset condensation, and (3) adaptation of condensation methods to the space-time domain and fair comparisons among them. From this study, we derive several intriguing observations: (i) sample diversity appears to be more crucial than temporal diversity for video dataset condensation, (ii) simple slide-window sampling proves to be effective, and (iii) sample selection currently outperforms dataset distillation in most cases. Furthermore, we conduct experiments on three prominent action recognition datasets (HMDB51, UCF101 and Kinetics-400) and achieve state-of-the-art results on all of them. Our code is available at https://github.com/MCG-NJU/Video-DC.
Code (1)
Tasks
Action RecognitionDataset CondensationDataset DistillationDiversitySimilar Papers 제목 키워드 기반
Large-scale weakly-supervised pre-training for video action recognition
Current fully-supervised video datasets consist of only a few hundred thousand videos and fewer than a thousand domain-specific labels. This hinders the progress towards advanced video architectures. This paper presents …
Action ClassificationAction RecognitionActivity RecognitionActivity Recognition In Videos+3Large-scale Robustness Analysis of Video Action Recognition Models
We have seen a great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance o…
Action RecognitionTemporal Action LocalizationA Large-Scale Robustness Analysis of Video Action Recognition Models
We have seen great progress in video action recognition in recent years. There are several models based on convolutional neural network (CNN) and some recent transformer based approaches which provide top performance…
Action RecognitionTemporal Action LocalizationEEV: A Large-Scale Dataset for Studying Evoked Expressions from Video
Videos can evoke a range of affective responses in viewers. The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and video recommendation. We introduce the Evoke…
DiversityRecommendation SystemsTransfer LearningVideo UnderstandingActionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
The problem of task planning for artificial agents remains largely unsolved. While there has been increasing interest in data-driven approaches for the study of task planning for artificial agents, a significant remainin…
Dataset GenerationTask Planning