Few-shot (FS) action recognition is a challenging com-
puter vision problem, where the task is to classify an unlabelled query video into one of the action categories in the support set having limited samples per action class.
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UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240. Source: Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification Image Source: https://www.crcv.ucf.edu/data/UCF101.php
The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance. Source: Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors Image Source: https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database
Kinetics-100 is a dataset split created from the Kinetics dataset to evaluate the performance of few-shot action recognition models. 100 classes are randomly selected from a total of 400 categories, each composed of 100 examples. The 100 classes are further split into 64, 12, and 24 non-overlapping classes to use as the meta-training set, meta-validation set, and meta-testing set, respectively. Link to the selected samples can be found here: https://github.com/ffmpbgrnn/CMN/tree/master/kinetics-100
Something-Something-100 is a dataset split created from Something-Something V2. A total of 100 classes are selected and each comprises 100 samples. The 100 classes were split into 64, 12, and 24 non-overlapping classes to use as the meta-training set, meta-validation set, and meta-testing set, respectively. Link to exactly selected samples can be found here: https://github.com/ffmpbgrnn/CMN/tree/master/smsm-100
A dataset dedicated to multi-object, multi-actor activity parsing. The dataset contains * Video-level labels (activities) * Segment-level labels (sub-activities) * Atomic actions (spatio-temporal scene graph) The scene graph annotations contain object/actor classes and bounding boxes, relationship annotations, and object/actor attributes.