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 176200 of 2759 papers

TitleStatusHype
ARID: A New Dataset for Recognizing Action in the DarkCode1
Benchmarking Micro-action Recognition: Dataset, Methods, and ApplicationsCode1
AR-Net: Adaptive Frame Resolution for Efficient Action RecognitionCode1
Disentangled Non-Local Neural NetworksCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
End-to-End Learning of Visual Representations from Uncurated Instructional VideosCode1
BMN: Boundary-Matching Network for Temporal Action Proposal GenerationCode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action RecognitionCode1
EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language modelsCode1
ACTION-Net: Multipath Excitation for Action RecognitionCode1
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIPCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
Bringing Online Egocentric Action Recognition into the wildCode1
3D CNNs with Adaptive Temporal Feature ResolutionsCode1
Full-Body Articulated Human-Object InteractionCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
CIDEr: Consensus-based Image Description EvaluationCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
CLIP-guided Prototype Modulating for Few-shot Action RecognitionCode1
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the WildCode1
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified