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

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Showing 2201–2250 of 2759 papers

TitleStatusHype
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
Representation Flow for Action RecognitionCode0
Interpretable Spatio-temporal Attention for Video Action Recognition—0
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural NetworksCode0
Part-based Graph Convolutional Network for Action RecognitionCode0
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding—0
Temporal-Spatial Mapping for Action Recognition—0
Using phase instead of optical flow for action recognitionCode0
Unsupervised Learning of View-invariant Action RepresentationsCode0
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition—0
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams—0
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—0
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video—0
Dividing and Aggregating Network for Multi-view Action Recognition—0
End-to-End Joint Semantic Segmentation of Actors and Actions in Video—0
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Deep Bilinear Learning for RGB-D Action Recognition—0
RESOUND: Towards Action Recognition without Representation Bias—0
Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos—0
Targeted Nonlinear Adversarial Perturbations in Images and VideosCode0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Temporal Sequence Distillation: Towards Few-Frame Action Recognition in Videos—0
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory—0
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors—0
Analyzing Human-Human Interactions: A SurveyCode0
Multi-Fiber Networks for Video Recognition—0
Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI—0
Actor-Centric Relation Network—0
Motion Feature Network: Fixed Motion Filter for Action Recognition—0
A Better Baseline for AVA—0
Contrastive Video Representation Learning via Adversarial Perturbations—0
Deep Discriminative Model for Video Classification—0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition—0
Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm—0
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks—0
The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences—0
Adding Attentiveness to the Neurons in Recurrent Neural Networks—0
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention—0
A Variational Time Series Feature Extractor for Action PredictionCode0
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning—0
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization—0
Action Recognition for Depth Video using Multi-view Dynamic ImagesCode0
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding—0
YH Technologies at ActivityNet Challenge 2018—0
Human Action Recognition and Prediction: A Survey—0
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition—0
CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps—0
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints—0
Modality Distillation with Multiple Stream Networks for Action RecognitionCode0
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

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
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-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
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified