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 11761200 of 2759 papers

TitleStatusHype
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Make A Long Image Short: Adaptive Token Length for Vision Transformers0
Self-supervised Video TransformerCode1
BEVT: BERT Pretraining of Video TransformersCode1
Stacked Temporal Attention: Improving First-person Action Recognition by Emphasizing Discriminative Clips0
MViTv2: Improved Multiscale Vision Transformers for Classification and DetectionCode1
Graph Convolutional Module for Temporal Action Localization in Videos0
MAU: A Motion-Aware Unit for Video Prediction and BeyondCode1
Your head is there to move you around: Goal-driven models of the primate dorsal pathway0
Dynamic Normalization and Relay for Video Action RecognitionCode0
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks0
Reformulating Zero-shot Action Recognition for Multi-label Actions0
Anonymization for Skeleton Action RecognitionCode1
Learning from Temporal Gradient for Semi-supervised Action RecognitionCode1
Cross Your Body: A Cognitive Assessment System for Children0
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
Florence: A New Foundation Model for Computer VisionCode1
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition0
Action Recognition with Domain Invariant Features of Skeleton Image0
Evaluating Transformers for Lightweight Action Recognition0
M2A: Motion Aware Attention for Accurate Video Action RecognitionCode1
Real-time 3D human action recognition based on Hyperpoint sequenceCode1
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly DetectionCode1
A Central Difference Graph Convolutional Operator for Skeleton-Based Action RecognitionCode0
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition0
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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