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 426450 of 2759 papers

TitleStatusHype
Bringing Online Egocentric Action Recognition into the wildCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
Referring Atomic Video Action RecognitionCode1
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIPCode1
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and DataCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
ResFormer: Scaling ViTs with Multi-Resolution TrainingCode1
DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action RecognitionCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video LearningCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
D^2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action RecognitionCode1
Data Efficient Video Transformer for Violence DetectionCode1
A Closer Look at Spatiotemporal Convolutions for Action RecognitionCode1
Concatenated Masked Autoencoders as Spatial-Temporal LearnerCode1
CT-Net: Channel Tensorization Network for Video ClassificationCode1
Project RISE: Recognizing Industrial Smoke EmissionsCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
RSPNet: Relative Speed Perception for Unsupervised Video Representation LearningCode1
A Local-to-Global Approach to Multi-modal Movie Scene SegmentationCode1
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational AutoencodersCode1
SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning StabilizationCode1
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World VideosCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensorsCode1
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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