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 2151–2200 of 2759 papers

TitleStatusHype
Compressing Recurrent Neural Networks with Tensor Ring for Action RecognitionCode1
Higher-order Network for Action Recognition—0
Relational Long Short-Term Memory for Video Action Recognition—0
A Perceptual Prediction Framework for Self Supervised Event SegmentationCode0
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention—0
Cross and Learn: Cross-Modal Self-SupervisionCode0
Leveraging Random Label Memorization for Unsupervised Pre-Training—0
StNet: Local and Global Spatial-Temporal Modeling for Action RecognitionCode0
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition—0
Random Temporal Skipping for Multirate Video Analysis—0
DeepGRU: Deep Gesture Recognition UtilityCode0
Informed Democracy: Voting-based Novelty Detection for Action Recognition—0
ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition—0
A^2-Nets: Double Attention Networks—0
Real-time Action Recognition with Dissimilarity-based Training of Specialized Module Networks—0
Fine-grained Video Categorization with Redundancy Reduction Attention—0
Learning with privileged information via adversarial discriminative modality distillationCode0
Cross-Modal and Hierarchical Modeling of Video and TextCode0
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANsCode1
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
Temporal-Spatial Mapping for Action Recognition—0
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding—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
RESOUND: Towards Action Recognition without Representation Bias—0
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video—0
End-to-End Joint Semantic Segmentation of Actors and Actions in Video—0
Deep Bilinear Learning for RGB-D Action Recognition—0
Dividing and Aggregating Network for Multi-view Action Recognition—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
Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos—0
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the WildCode1
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
Attributes' Importance for Zero-Shot Pose-Classification Based on Wearable Sensors—0
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory—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
A Better Baseline for AVA—0
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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