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 201225 of 2759 papers

TitleStatusHype
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action RepresentationsCode1
Learning Discriminative Representations for Skeleton Based Action RecognitionCode1
CLIP-guided Prototype Modulating for Few-shot Action RecognitionCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight OptimizationCode1
Epic-Sounds: A Large-scale Dataset of Actions That SoundCode1
Graph Contrastive Learning for Skeleton-based Action RecognitionCode1
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action RecognitionCode1
HierVL: Learning Hierarchical Video-Language EmbeddingsCode1
Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic CompressionCode1
Transformer-Based Unified Recognition of Two Hands Manipulating ObjectsCode1
Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action RecognitionCode1
TempCLR: Temporal Alignment Representation with Contrastive LearningCode1
Full-Body Articulated Human-Object InteractionCode1
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action RecognitionCode1
Towards Holistic Surgical Scene UnderstandingCode1
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation LearningCode1
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video LearningCode1
Hierarchical Contrast for Unsupervised Skeleton-based Action Representation LearningCode1
ResFormer: Scaling ViTs with Multi-Resolution TrainingCode1
Towards Good Practices for Missing Modality Robust Action RecognitionCode1
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing AugmentationsCode1
Video Test-Time Adaptation for Action RecognitionCode1
SVFormer: Semi-supervised Video Transformer for Action RecognitionCode1
Mitigating and Evaluating Static Bias of Action Representations in the Background and the ForegroundCode1
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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