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 20262050 of 2759 papers

TitleStatusHype
Cross Domain Model Compression by Structurally Weight Sharing0
Collaborative Spatiotemporal Feature Learning for Video Action RecognitionCode0
3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks0
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video ArchitecturesCode0
What Makes Training Multi-Modal Classification Networks Hard?Code0
Rethinking Full Connectivity in Recurrent Neural Networks0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks0
Hierarchical Feature Aggregation Networks for Video Action Recognition0
Examining Interpretable Feature Relationships in Deep Networks for Action recognition0
Unsupervised Learning from Video with Deep Neural EmbeddingsCode0
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
Exploring Temporal Information for Improved Video UnderstandingCode0
What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality AttentionCode1
Lightweight Network Architecture for Real-Time Action RecognitionCode1
Learning Video Representations from Correspondence ProposalsCode0
Richly Activated Graph Convolutional Network for Action Recognition with Incomplete SkeletonsCode0
Towards a Skeleton-Based Action Recognition For Realistic Scenarios0
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity UnderstandingCode1
Large-scale weakly-supervised pre-training for video action recognitionCode0
Human Action Recognition with Deep Temporal Pyramids0
Egocentric Hand Track and Object-based Human Action Recognition0
Where and when to look? Spatial-temporal attention for action recognition in videos0
Unseen Action Recognition with Unpaired Adversarial Multimodal Learning0
Human Action Recognition Based on Spatial-Temporal Attention0
Memory-Augmented Temporal Dynamic Learning for Action Recognition0
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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