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 17511775 of 2759 papers

TitleStatusHype
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?0
Audio-Visual Instance Discrimination with Cross-Modal AgreementCode1
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action RecognitionCode1
Action recognition in real-world videos0
Human and Machine Action Prediction Independent of Object Information0
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition0
Combining Deep Learning Classifiers for 3D Action Recognition0
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition0
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding0
SpeedNet: Learning the Speediness in VideosCode1
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View0
ASL Recognition with Metric-Learning based Lightweight Network0
Improved Residual Networks for Image and Video RecognitionCode1
Temporal Pyramid Network for Action RecognitionCode1
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition0
Human action recognition with a large-scale brain-inspired photonic computer0
A Local-to-Global Approach to Multi-modal Movie Scene SegmentationCode1
Deep Multimodal Feature Encoding for Video OrderingCode1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
TEA: Temporal Excitation and Aggregation for Action RecognitionCode1
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention0
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
Speech2Action: Cross-modal Supervision for Action Recognition0
Omni-sourced Webly-supervised Learning for Video RecognitionCode2
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
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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