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 17761800 of 2759 papers

TitleStatusHype
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks0
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks0
Action Recognition based on Subdivision-Fusion Model0
Action Recognition by Hierarchical Mid-level Action Elements0
Action Recognition by Hierarchical Sequence Summarization0
Action recognition by learning pose representations0
Action Recognition for American Sign Language0
Action recognition from depth maps using deep convolutional neural networks0
Action Recognition: From Static Datasets to Moving Robots0
Action recognition in real-world videos0
Action recognition in still images by latent superpixel classification0
Action Recognition in the Frequency Domain0
Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework0
Action Recognition in Video Recordings from Gynecologic Laparoscopy0
Action Recognition in Video Using Sparse Coding and Relative Features0
Action Recognition Using Supervised Spiking Neural Networks0
Action Recognition using Transfer Learning and Majority Voting for CSGO0
Action Recognition Utilizing YGAR Dataset0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision0
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion0
Action Recognition with Deep Multiple Aggregation Networks0
Action Recognition with Domain Invariant Features of Skeleton Image0
Action Recognition with Image Based CNN Features0
Action Recognition with Joint Attention on Multi-Level Deep Features0
Action Recognition with Kernel-based Graph Convolutional Networks0
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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