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 51–100 of 124 papers

TitleStatusHype
Learning Video Representations from Correspondence ProposalsCode0
Learn to cycle: Time-consistent feature discovery for action recognitionCode0
MMTM: Multimodal Transfer Module for CNN FusionCode0
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action RecognitionCode0
Out-of-Distribution Detection for Generalized Zero-Shot Action RecognitionCode0
Pose And Joint-Aware Action RecognitionCode0
R-C3D: Region Convolutional 3D Network for Temporal Activity DetectionCode0
Representation Flow for Action RecognitionCode0
Resource Efficient 3D Convolutional Neural NetworksCode0
Robust Real-Time Violence Detection in Video Using CNN And LSTMCode0
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in VideosCode0
Self-Supervised MultiModal Versatile NetworksCode0
Temporal Relational Reasoning in VideosCode0
Towards Improving Spatiotemporal Action Recognition in VideosCode0
Two-Stream Convolutional Networks for Action Recognition in VideosCode0
Two-stream Flow-guided Convolutional Attention Networks for Action RecognitionCode0
UCF101: A Dataset of 101 Human Actions Classes From Videos in The WildCode0
Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet TransformerCode0
Video Transformer NetworkCode0
What Makes Training Multi-Modal Classification Networks Hard?Code0
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action LocalizationCode0
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition—0
Action Class Relation Detection and Classification Across Multiple Video Datasets—0
A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection—0
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
A Key Volume Mining Deep Framework for Action Recognition—0
Where and when to look? Spatial-temporal attention for action recognition in videos—0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos—0
Discriminative Video Representation Learning Using Support Vector Classifiers—0
Discriminative convolutional Fisher vector network for action recognition—0
Developing Motion Code Embedding for Action Recognition in Videos—0
DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding—0
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks—0
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues—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
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View—0
Could Giant Pretrained Image Models Extract Universal Representations?—0
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos—0
STM: SpatioTemporal and Motion Encoding for Action Recognition—0
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
Class incremental learning for video action classification—0
Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing—0
Temporal Difference Networks for Action Recognition—0
Action Recognition with Joint Attention on Multi-Level Deep Features—0
Class-Incremental Learning for Action Recognition in Videos—0
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice—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