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 23512375 of 2759 papers

TitleStatusHype
Free-Form Composition Networks for Egocentric Action Recognition0
Frequency-aware Event Cloud Network0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks0
From CNNs to Transformers in Multimodal Human Action Recognition: A Survey0
From Detection to Action Recognition: An Edge-Based Pipeline for Robot Human Perception0
From Image to Video: An Empirical Study of Diffusion Representations0
From Pose to Activity: Surveying Datasets and Introducing CONVERSE0
From Synsets to Videos: Enriching ItalWordNet Multimodally0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation0
FSD-10: A Dataset for Competitive Sports Content Analysis0
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition0
Fundamental Limits of Transfer Learning in Binary Classifications0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification0
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly0
Fusing multiple features for depth-based action recognition0
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification0
Future Aspects in Human Action Recognition: Exploring Emerging Techniques and Ethical Influences0
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition0
GCF-Net: Gated Clip Fusion Network for Video Action Recognition0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
Generalized Rank Pooling for Activity Recognition0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data0
Generating Action-conditioned Prompts for Open-vocabulary Video 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