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 1651–1700 of 2759 papers

TitleStatusHype
Reinforcement Learning meets Masked Video Modeling : Trajectory-Guided Adaptive Token Selection—0
Relational Action Forecasting—0
Relational Long Short-Term Memory for Video Action Recognition—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Representation Learning with Video Deep InfoMax—0
Representing Videos as Discriminative Sub-graphs for Action Recognition—0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
RESOUND: Towards Action Recognition without Representation Bias—0
REST: REtrieve & Self-Train for generative action recognition—0
Rethinking Full Connectivity in Recurrent Neural Networks—0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Rethinking matching-based few-shot action recognition—0
Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization—0
Retrieving and Highlighting Action with Spatiotemporal Reference—0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction—0
Review on Action Recognition for Accident Detection in Smart City Transportation Systems—0
Revisiting Human Action Recognition: Personalization vs. Generalization—0
Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition—0
RGB-D-based Action Recognition Datasets: A Survey—0
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks—0
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory—0
Riemannian batch normalization for SPD neural networks—0
RNN Fisher Vectors for Action Recognition and Image Annotation—0
RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem—0
RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model—0
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions—0
Robust Audio-Visual Instance Discrimination—0
Robust Estimation of 3D Human Poses from a Single Image—0
Robust features for facial action recognition—0
Robust Multi-body Feature Tracker: A Segmentation-free Approach—0
Robustness Evaluation of Machine Learning Models for Robot Arm Action Recognition in Noisy Environments—0
Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos—0
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data—0
RotaTR: Detection Transformer for Dense and Rotated Object—0
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition—0
R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition—0
S3Aug: Segmentation, Sampling, and Shift for Action Recognition—0
S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action Recognition—0
SAFCAR: Structured Attention Fusion for Compositional Action Recognition—0
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021—0
Sampling Strategies for Real-Time Action Recognition—0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching—0
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition—0
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines—0
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition—0
SCA Net: Sparse Channel Attention Module for Action Recognition—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks—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