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 76100 of 2759 papers

TitleStatusHype
A Closer Look at Spatiotemporal Convolutions for Action RecognitionCode1
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual DistillationCode1
CoCon: Cooperative-Contrastive LearningCode1
CIDEr: Consensus-based Image Description EvaluationCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
CLIP-guided Prototype Modulating for Few-shot Action RecognitionCode1
CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction AlignmentCode1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
ACTION-Net: Multipath Excitation for Action RecognitionCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
BEVT: BERT Pretraining of Video TransformersCode1
3D CNNs with Adaptive Temporal Feature ResolutionsCode1
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and DataCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Full-Body Articulated Human-Object InteractionCode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified