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 501525 of 2759 papers

TitleStatusHype
Concatenated Masked Autoencoders as Spatial-Temporal LearnerCode1
ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab0
IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like SettingCode1
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition0
Videoprompter: an ensemble of foundational models for zero-shot video understanding0
S3Aug: Segmentation, Sampling, and Shift for Action Recognition0
Is Weakly-supervised Action Segmentation Ready For Human-Robot Interaction? No, Let's Improve It With Action-union LearningCode2
Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey0
Deep Learning Techniques for Video Instance Segmentation: A Survey0
Frozen Transformers in Language Models Are Effective Visual Encoder LayersCode2
Few-shot Action Recognition with Captioning Foundation Models0
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action RecognitionCode1
Flow Dynamics Correction for Action Recognition0
3DYoga90: A Hierarchical Video Dataset for Yoga Pose UnderstandingCode1
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes0
SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition0
Language Model Beats Diffusion -- Tokenizer is Key to Visual GenerationCode4
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and DataCode1
Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks0
Graph learning in robotics: a survey0
PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to VideoCode1
Action Recognition Utilizing YGAR Dataset0
A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos0
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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