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 51–75 of 124 papers

TitleStatusHype
Coupled Recurrent Network (CRN)—0
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition—0
Deep Learning Approaches for Human Action Recognition in Video Data—0
DenseImage Network: Video Spatial-Temporal Evolution Encoding and Understanding—0
Developing Motion Code Embedding for Action Recognition in Videos—0
Discriminative convolutional Fisher vector network for action recognition—0
Discriminative Video Representation Learning Using Support Vector Classifiers—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web—0
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition—0
Dynamic Sampling Networks for Efficient Action Recognition in Videos—0
Evolving Space-Time Neural Architectures for Videos—0
Hierarchical Attention Network for Action Recognition in Videos—0
Knowledge Prompting for Few-shot Action Recognition—0
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition—0
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks—0
Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision—0
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition—0
Motion Feature Network: Fixed Motion Filter for Action Recognition—0
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition—0
Per-Sample Kernel Adaptation for Visual Recognition and Grouping—0
Pose-Based Two-Stream Relational Networks for Action Recognition in Videos—0
Pose from Action: Unsupervised Learning of Pose Features based on Motion—0
Procedural Generation of Videos to Train Deep Action Recognition Networks—0
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks—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
1MMNetX-Sub97.4—Unverified
2DSCNet (RGB + Pose)X-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