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 451475 of 2759 papers

TitleStatusHype
PAN: Towards Fast Action Recognition via Learning Persistence of AppearanceCode1
Location-aware Graph Convolutional Networks for Video Question AnsweringCode1
Self-supervised Temporal Discriminative Learning for Video Representation LearningCode1
Self-supervised learning using consistency regularization of spatio-temporal data augmentation for action recognitionCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Memory-augmented Dense Predictive Coding for Video Representation LearningCode1
SeCo: Exploring Sequence Supervision for Unsupervised Representation LearningCode1
RareAct: A video dataset of unusual interactionsCode1
Late Temporal Modeling in 3D CNN Architectures with BERT for Action RecognitionCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural NetworksCode1
Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action RecognitionCode1
RubiksNet: Learnable 3D-Shift for Efficient Video Action RecognitionCode1
DDGCN: A Dynamic Directed Graph Convolutional Network for Action RecognitionCode1
Multi-view Action Recognition using Cross-view Video PredictionCode1
AR-Net: Adaptive Frame Resolution for Efficient Action RecognitionCode1
LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task ActivitiesCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
Approximated Bilinear Modules for Temporal ModelingCode1
MotionSqueeze: Neural Motion Feature Learning for Video UnderstandingCode1
Context-Aware RCNN: A Baseline for Action Detection in VideosCode1
Region-based Non-local Operation for Video ClassificationCode1
Unsupervised 3D Human Pose Representation with Viewpoint and Pose DisentanglementCode1
TinyVIRAT: Low-resolution Video Action RecognitionCode1
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in 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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified