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 651675 of 2759 papers

TitleStatusHype
fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition0
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action RecognitionCode0
CVB: A Video Dataset of Cattle Visual Behaviors0
Deep Neural Networks in Video Human Action Recognition: A Review0
Cross-view Action Recognition Understanding From Exocentric to Egocentric Perspective0
High Speed Human Action Recognition using a Photonic Reservoir Computer0
Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning0
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action RecognitionCode1
SCP: Soft Conditional Prompt Learning for Aerial Video Action Recognition0
Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology AwarenessCode1
Riemannian Multinomial Logistics Regression for SPD Neural NetworksCode1
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark0
Learning Higher-order Object Interactions for Keypoint-based Video Understanding0
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
Is end-to-end learning enough for fitness activity recognition?0
M^2DAR: Multi-View Multi-Scale Driver Action Recognition with Vision TransformerCode1
Lightweight Delivery Detection on Doorbell Cameras0
MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless SensingCode1
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
MMG-Ego4D: Multi-Modal Generalization in Egocentric Action RecognitionCode0
Self-Supervised Video Representation Learning via Latent Time Navigation0
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization0
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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