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 476500 of 2759 papers

TitleStatusHype
LEAP: LLM-Generation of Egocentric Action Programs0
Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic ScenesCode1
Object-based (yet Class-agnostic) Video Domain Adaptation0
PALM: Predicting Actions through Language Models0
GeoDeformer: Geometric Deformable Transformer for Action Recognition0
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling0
F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning0
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition0
REACT: Recognize Every Action Everywhere All At Once0
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition0
Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer LearningCode1
UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning0
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
Multi-modal Instance Refinement for Cross-domain Action Recognition0
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain GapCode0
Modality Mixer Exploiting Complementary Information for Multi-modal Action Recognition0
VLM-Eval: A General Evaluation on Video Large Language Models0
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System0
Learning Human Action Recognition Representations Without Real HumansCode0
Semantic-aware Video Representation for Few-shot Action Recognition0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
FPGA-QHAR: Throughput-Optimized for Quantized Human Action Recognition on The EdgeCode0
Distilling Knowledge from CNN-Transformer Models for Enhanced 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