SOTAVerified

Action Recognition In Videos

Action Recognition in Videos is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.

Papers

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Showing 1–50 of 124 papers

TitleStatusHype
A new face swap method for image and video domains: a technical reportCode3
VideoMAE V2: Scaling Video Masked Autoencoders with Dual MaskingCode2
Learning Spatiotemporal Features with 3D Convolutional NetworksCode2
Temporal Segment Networks: Towards Good Practices for Deep Action RecognitionCode2
Temporal Segment Networks for Action Recognition in VideosCode2
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D VideosCode1
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in VideosCode1
Self-supervised Video TransformerCode1
Unsupervised Learning of Video Representations via Dense Trajectory ClusteringCode1
TDN: Temporal Difference Networks for Efficient Action RecognitionCode1
Learning Implicit Temporal Alignment for Few-shot Video ClassificationCode1
Tensor Representations for Action RecognitionCode1
DirecFormer: A Directed Attention in Transformer Approach to Robust Action RecognitionCode1
Florence: A New Foundation Model for Computer VisionCode1
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and TextCode1
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in VideosCode1
Self-supervised Video Representation Learning with Cross-Stream Prototypical ContrastingCode1
SlowFast Networks for Video RecognitionCode1
Actor-agnostic Multi-label Action Recognition with Multi-modal QueryCode1
Spatiotemporal Residual Networks for Video Action RecognitionCode1
TEA: Temporal Excitation and Aggregation for Action RecognitionCode1
Towards Good Practices for Very Deep Two-Stream ConvNetsCode1
Region-based Non-local Operation for Video ClassificationCode1
YouTube-8M: A Large-Scale Video Classification BenchmarkCode1
ActionCLIP: A New Paradigm for Video Action RecognitionCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
Multimodal Fusion via Teacher-Student Network for Indoor Action RecognitionCode1
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video LearningCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Space-time Mixing Attention for Video TransformerCode1
Busy-Quiet Video Disentangling for Video ClassificationCode1
Dual-path Adaptation from Image to Video TransformersCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
Multi-Temporal Convolutions for Human Action Recognition in VideosCode1
Self-supervised Video Representation Learning Using Inter-intra Contrastive FrameworkCode1
EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action RecognitionCode1
Discriminative Video Representation Learning Using Support Vector Classifiers—0
Discriminative convolutional Fisher vector network for action recognition—0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos—0
Developing Motion Code Embedding for Action Recognition in Videos—0
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice—0
Per-Sample Kernel Adaptation for Visual Recognition and Grouping—0
DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding—0
Deep Learning Approaches for Human Action Recognition in Video Data—0
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition—0
Coupled Recurrent Network (CRN)—0
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition—0
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos—0
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition—0
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All datasetsJester (Gesture Recognition)PKU-MMDUCF101Something-Something V2Kinetics 400AVA v2.2FS-Something-Something V2-FullFS-Something-Something V2-SmallSports-1MTHUMOS14ActivityNetAVA v2.1

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPNet Res34, 5 CPVal96.7—Unverified
2STM (Resnet-50, 16 frames)Val96.7—Unverified
3MFNetVal96.68—Unverified
4DINVal95.31—Unverified
5MultiScale TRNVal95.31—Unverified
6convSTARVal92.7—Unverified
73D-SqueezeNetVal90.77—Unverified
83D-ShuffleNetV2 0.25xVal86.91—Unverified
93D-MobileNetV2 0.2xVal86.43—Unverified
#ModelMetricClaimedVerifiedStatus
1DSCNet (RGB + Pose)X-Sub97.4—Unverified
2MMNetX-Sub97.4—Unverified
3EPAM-NetX-Sub96.2—Unverified
4DVANet (RGB only)X-Sub95.8—Unverified
5TSMFX-Sub95.8—Unverified
#ModelMetricClaimedVerifiedStatus
1STM (ImageNet+Kinetics pretrain)3-fold Accuracy96.2—Unverified
23D-SqueezeNet3-fold Accuracy74.94—Unverified
33D-ShuffleNetV2 0.25x3-fold Accuracy56.52—Unverified
43D-MobileNetV2 0.2x3-fold Accuracy55.56—Unverified
5Baseline UCF1013-fold Accuracy43.9—Unverified
#ModelMetricClaimedVerifiedStatus
1STM (16 frames, ImageNet pretraining)Top-1 Accuracy64.2—Unverified
2CPNet Res34, 5 CPTop-1 Accuracy57.65—Unverified
32-Stream TRNTop-1 Accuracy55.52—Unverified
4DINTop-1 Accuracy34.11—Unverified
#ModelMetricClaimedVerifiedStatus
1FlorenceTop-1 Accuracy86.5—Unverified
2ActionCLIP (ViT-B/16)Top-1 Accuracy83.8—Unverified
3Frozen Backbone, SwinV2-G-ext22K (Video-Swin)Top-1 Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1YOWO+LFB*mAP (Val)20.2—Unverified
2VideoMAE V2mAP (Val)18.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ITANetTop-1 Accuracy(5-Way-1-Shot)49.2—Unverified
2OTAM[3]++Top-1 Accuracy(5-Way-1-Shot)42.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ITANetTop-1 Accuracy(5-Way-1-Shot)39.8—Unverified
2CMN[35]Top-1 Accuracy(5-Way-1-Shot)36.2—Unverified
#ModelMetricClaimedVerifiedStatus
1G-BlendVideo hit@174.8—Unverified
2LSTM +Pretrained on YT-8MVideo hit@165.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Single-stream R-C3D (two-way buffer)mAP@0.154.5—Unverified
2Single-stream R-C3D (one-way buffer)mAP@0.151.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM + Pretrained on YT-8MmAP75.6—Unverified
#ModelMetricClaimedVerifiedStatus
1YOWO+LFB*mAP (Val)19.2—Unverified
#ModelMetricClaimedVerifiedStatus
1STM (ImageNet+Kinetics pretrain)Average accuracy of 3 splits72.2—Unverified
#ModelMetricClaimedVerifiedStatus
1FlorenceTop-1 Accuracy87.8—Unverified
#ModelMetricClaimedVerifiedStatus
1G-BlendClip Hit@149.7—Unverified
#ModelMetricClaimedVerifiedStatus
12D-3D-Softargmax (RGB only)Accuracy (CS)85.5—Unverified
#ModelMetricClaimedVerifiedStatus
1STM (16 frames, ImageNet pretraining)Top 1 Accuracy50.7—Unverified