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

Showing 14761500 of 2759 papers

TitleStatusHype
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition0
Normalized Human Pose Features for Human Action Video Alignment0
Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks0
Learning Self-Similarity in Space and Time as a Generalized Motion for Action RecognitionCode1
Learning Visual Representation from Human Interactions0
Temporal Difference Networks for Action Recognition0
Beyond the Pixels: Exploring the Effects of Bit-Level Network and File Corruptions on Video Model Robustness0
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs0
3D Human motion anticipation and classification0
2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition0
Tensor Representations for Action RecognitionCode1
Action Recognition with Kernel-based Graph Convolutional Networks0
Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action RecognitionCode1
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain0
Human Action Recognition from Various Data Modalities: A Review0
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences0
Recent Advances of Generic Object Detection with Deep Learning: A Review0
SMART Frame Selection for Action Recognition0
TDN: Temporal Difference Networks for Efficient Action RecognitionCode1
Smoothed Gaussian Mixture Models for Video Classification and Recommendation0
Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN0
Temporal Graph Modeling for Skeleton-based Action Recognition0
NUTA: Non-uniform Temporal Aggregation for Action Recognition0
Towards Improving Spatiotemporal Action Recognition in VideosCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3Unverified
4DejaVidTop-1 Accuracy77.2Unverified
5InternVideoTop-1 Accuracy77.2Unverified
6InternVideo2-1BTop-1 Accuracy77.1Unverified
7VideoMAE V2-gTop-1 Accuracy77Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5Unverified
10TubeViT-LTop-1 Accuracy76.1Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7Unverified
2OmniVec23-fold Accuracy99.6Unverified
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
10LGD-3D Two-stream3-fold Accuracy98.2Unverified