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
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and AlgorithmsCode1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
EZ-CLIP: Efficient Zeroshot Video Action RecognitionCode1
Federated Self-supervised Learning for Video UnderstandingCode1
Few-shot Action Recognition with Prototype-centered Attentive LearningCode1
Fisher Information guided Purification against Backdoor AttacksCode1
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action RecognitionCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
FreqMixFormerV2: Lightweight Frequency-aware Mixed Transformer for Human Skeleton Action RecognitionCode1
Bringing Online Egocentric Action Recognition into the wildCode1
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIPCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
A Lie Group Approach to Riemannian Batch NormalizationCode1
Grad-CAM++: Improved Visual Explanations for Deep Convolutional NetworksCode1
Graph Contrastive Learning for Skeleton-based Action RecognitionCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Group Contextualization for Video RecognitionCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
A Local-to-Global Approach to Multi-modal Movie Scene SegmentationCode1
Hierarchical Contrast for Unsupervised Skeleton-based Action Representation LearningCode1
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
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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