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 20262050 of 2759 papers

TitleStatusHype
Video action recognition for lane-change classification and prediction of surrounding vehicles0
Video Action Recognition Using spatio-temporal optical flow video frames0
Video Action Recognition Via Neural Architecture Searching0
Video Action Recognition with Attentive Semantic Units0
Video Action Transformer Network0
Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset0
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories0
Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis0
Video-based Human Action Recognition using Deep Learning: A Review0
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention0
Perceptron Synthesis Network: Rethinking the Action Scale Variances in Videos0
Video Domain Incremental Learning for Human Action Recognition in Home Environments0
VideoGLUE: Video General Understanding Evaluation of Foundation Models0
Video Is Graph: Structured Graph Module for Video Action Recognition0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition0
VideoLightFormer: Lightweight Action Recognition using Transformers0
Video Modeling with Correlation Networks0
Texture-Based Input Feature Selection for Action Recognition0
Videoprompter: an ensemble of foundational models for zero-shot video understanding0
Video Representation Learning by Recognizing Temporal Transformations0
Video Representation Learning Using Discriminative Pooling0
Video-ReTime: Learning Temporally Varying Speediness for Time Remapping0
Video RWKV:Video Action Recognition Based RWKV0
Videos as Space-Time Region Graphs0
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network0
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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