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 401425 of 2759 papers

TitleStatusHype
Full-Body Articulated Human-Object InteractionCode1
PSTNet: Point Spatio-Temporal Convolution on Point Cloud SequencesCode1
Florence: A New Foundation Model for Computer VisionCode1
FLAVR: Flow-Agnostic Video Representations for Fast Frame InterpolationCode1
Quo Vadis, Skeleton Action Recognition ?Code1
RareAct: A video dataset of unusual interactionsCode1
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion SimilarityCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
CIDEr: Consensus-based Image Description EvaluationCode1
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in VideosCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
Referring Atomic Video Action RecognitionCode1
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Grad-CAM++: Improved Visual Explanations for Deep Convolutional NetworksCode1
Generative Model-based Feature Knowledge Distillation for Action RecognitionCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action RecognitionCode1
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video LearningCode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
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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