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 5175 of 2759 papers

TitleStatusHype
HAKE: A Knowledge Engine Foundation for Human Activity UnderstandingCode2
UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormerCode2
AdaptFormer: Adapting Vision Transformers for Scalable Visual RecognitionCode2
VideoMAE V2: Scaling Video Masked Autoencoders with Dual MaskingCode2
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action RecognitionCode2
Deep Architectures for Content Moderation and Movie Content RatingCode2
Hulk: A Universal Knowledge Translator for Human-Centric TasksCode2
Egocentric Video-Language PretrainingCode2
Leveraging Temporal Contextualization for Video Action RecognitionCode2
Temporal Action Detection with Structured Segment NetworksCode2
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
3D Human Action Representation Learning via Cross-View Consistency PursuitCode1
A Comprehensive Study of Deep Video Action RecognitionCode1
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIPCode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
Bringing Online Egocentric Action Recognition into the wildCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
A Closer Look at Spatiotemporal Convolutions for Action RecognitionCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
Benchmarking Micro-action Recognition: Dataset, Methods, and ApplicationsCode1
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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