SOTAVerified

Action Recognition

Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.

In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.

Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.

Papers

Showing 11011125 of 2759 papers

TitleStatusHype
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs0
DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos0
Developing Motion Code Embedding for Action Recognition in Videos0
ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition0
2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework0
Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych0
Detection of Manipulation Action Consequences (MAC)0
Audio-Visual Contrastive Learning with Temporal Self-Supervision0
Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition0
Self-supervised Contrastive Learning for Audio-Visual Action Recognition0
Adaptive RNN Tree for Large-Scale Human Action Recognition0
A Two-stream Neural Network for Pose-based Hand Gesture Recognition0
Detecting Hands in Egocentric Videos: Towards Action Recognition0
Adaptive Recursive Circle Framework for Fine-grained Action Recognition0
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories0
Improved Dense Trajectory with Cross Streams0
Improving Zero-Shot Action Recognition using Human Instruction with Text Description0
Improving Interpretability of Deep Neural Networks with Semantic Information0
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks0
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors0
Depth-Aware Action Recognition: Pose-Motion Encoding through Temporal Heatmaps0
Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3Unverified
4DejaVidTop-1 Accuracy77.2Unverified
5InternVideoTop-1 Accuracy77.2Unverified
6InternVideo2-1BTop-1 Accuracy77.1Unverified
7VideoMAE V2-gTop-1 Accuracy77Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5Unverified
10TubeViT-LTop-1 Accuracy76.1Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7Unverified
2OmniVec23-fold Accuracy99.6Unverified
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
10LGD-3D Two-stream3-fold Accuracy98.2Unverified