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 101125 of 2759 papers

TitleStatusHype
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
CIDEr: Consensus-based Image Description EvaluationCode1
CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction AlignmentCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
A Closer Look at Spatiotemporal Convolutions for Action RecognitionCode1
Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action RecognitionCode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensorsCode1
D^2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action RecognitionCode1
DDGCN: A Dynamic Directed Graph Convolutional Network for Action RecognitionCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Deep Analysis of CNN-based Spatio-temporal Representations for Action RecognitionCode1
Bringing Online Egocentric Action Recognition into the wildCode1
A Comprehensive Study of Deep Video Action RecognitionCode1
Actor-Context-Actor Relation Network for Spatio-Temporal Action LocalizationCode1
3D Human Action Representation Learning via Cross-View Consistency PursuitCode1
DEVIAS: Learning Disentangled Video Representations of Action and SceneCode1
Disentangled Non-Local Neural NetworksCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and DataCode1
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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