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 126150 of 2759 papers

TitleStatusHype
Encoding Surgical Videos as Latent Spatiotemporal Graphs for Object and Anatomy-Driven ReasoningCode1
Action knowledge for video captioning with graph neural networksCode1
CLIP-guided Prototype Modulating for Few-shot Action RecognitionCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
Full-Body Articulated Human-Object InteractionCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
3DYoga90: A Hierarchical Video Dataset for Yoga Pose UnderstandingCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
Action Genome: Actions as Composition of Spatio-temporal Scene GraphsCode1
3DV: 3D Dynamic Voxel for Action Recognition in Depth VideoCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and DataCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
CIAGAN: Conditional Identity Anonymization Generative Adversarial NetworksCode1
Context-Aware RCNN: A Baseline for Action Detection in VideosCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
Action-Conditioned 3D Human Motion Synthesis with Transformer VAECode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Bringing Online Egocentric Action Recognition into the wildCode1
ActionCLIP: A New Paradigm for Video Action RecognitionCode1
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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