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 51–75 of 2759 papers

TitleStatusHype
BlockGCN: Redefine Topology Awareness for Skeleton-Based Action RecognitionCode2
UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormerCode2
EgoVideo: Exploring Egocentric Foundation Model and Downstream AdaptationCode2
Egocentric Video-Language PretrainingCode2
Dynamic 3D Point Cloud Sequences as 2D VideosCode2
ActionFormer: Localizing Moments of Actions with TransformersCode2
Deep Architectures for Content Moderation and Movie Content RatingCode2
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality AssessmentCode2
Leveraging Temporal Contextualization for Video Action RecognitionCode2
Temporal Segment Networks for Action Recognition in VideosCode2
A Multigrid Method for Efficiently Training Video ModelsCode1
A Local-to-Global Approach to Multi-modal Movie Scene SegmentationCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
3D Human Action Representation Learning via Cross-View Consistency PursuitCode1
A Comprehensive Study of Deep Video Action RecognitionCode1
A Lie Group Approach to Riemannian Batch NormalizationCode1
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action RecognitionCode1
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and DataCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIPCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
A Closer Look at Spatiotemporal Convolutions for Action RecognitionCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4InternVideoTop-1 Accuracy77.2—Unverified
5DejaVidTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec3-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec23-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
10Text4Vis3-fold Accuracy98.2—Unverified