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 576600 of 2759 papers

TitleStatusHype
Enlarging Instance-specific and Class-specific Information for Open-set Action RecognitionCode1
ARID: A New Dataset for Recognizing Action in the DarkCode1
Multi-Granularity Hand Action DetectionCode1
Multimodal Visual Concept Learning with Weakly Supervised TechniquesCode0
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video UnderstandingCode0
Multimodal Task-Driven Dictionary Learning for Image ClassificationCode0
Multiple Human Tracking using Multi-Cues including Primitive Action FeaturesCode0
A New Split for Evaluating True Zero-Shot Action RecognitionCode0
Comparative Analysis: Violence Recognition from Videos using Transfer LearningCode0
Comparative Analysis of CNN-based Spatiotemporal Reasoning in VideosCode0
Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognitionCode0
Multi class activity classification in videos using Motion History Image generationCode0
Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image DatasetsCode0
Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?Code0
Multi-attention Networks for Temporal Localization of Video-level LabelsCode0
Multi-level Second-order Few-shot LearningCode0
Action Recognition Using Volumetric Motion RepresentationsCode0
Multimodal Attack Detection for Action Recognition ModelsCode0
Collaborative Spatiotemporal Feature Learning for Video Action RecognitionCode0
Collaborative Spatio-temporal Feature Learning for Video Action RecognitionCode0
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
Action Recognition using Visual AttentionCode0
MOFO: MOtion FOcused Self-Supervision for Video UnderstandingCode0
Moments in Time Dataset: one million videos for event understandingCode0
MaCLR: Motion-aware Contrastive Learning of Representations for VideosCode0
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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