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
An Image is Worth 16x16 Words, What is a Video Worth?Code1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior UnderstandingCode1
Can An Image Classifier Suffice For Action Recognition?Code1
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Concatenated Masked Autoencoders as Spatial-Temporal LearnerCode1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
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
CLIP-guided Prototype Modulating for Few-shot Action RecognitionCode1
An Evaluation of Action Recognition Models on EPIC-KitchensCode1
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual DistillationCode1
Complex Sequential Understanding through the Awareness of Spatial and Temporal ConceptsCode1
Volterra Neural Networks (VNNs)Code1
Cross-Architecture Self-supervised Video Representation LearningCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
ACTION-Net: Multipath Excitation for Action RecognitionCode1
Full-Body Articulated Human-Object InteractionCode1
3D CNNs with Adaptive Temporal Feature ResolutionsCode1
An Action Is Worth Multiple Words: Handling Ambiguity in Action RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
CIDEr: Consensus-based Image Description EvaluationCode1
CoCon: Cooperative-Contrastive LearningCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
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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