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 401425 of 2759 papers

TitleStatusHype
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action RecognitionCode1
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human ActionsCode1
TCLR: Temporal Contrastive Learning for Video RepresentationCode1
Few-shot Action Recognition with Prototype-centered Attentive LearningCode1
ArtEmis: Affective Language for Visual ArtCode1
Temporal-Relational CrossTransformers for Few-Shot Action RecognitionCode1
Temporally Guided Articulated Hand Pose Tracking in Surgical VideosCode1
Refining activation downsampling with SoftPoolCode1
Learning Self-Similarity in Space and Time as a Generalized Motion for Action RecognitionCode1
Tensor Representations for Action RecognitionCode1
Skeleton-DML: Deep Metric Learning for Skeleton-Based One-Shot Action RecognitionCode1
TDN: Temporal Difference Networks for Efficient Action RecognitionCode1
FLAVR: Flow-Agnostic Video Representations for Fast Frame InterpolationCode1
Temporal Relational Modeling with Self-Supervision for Action SegmentationCode1
MSAF: Multimodal Split Attention FusionCode1
MVFNet: Multi-View Fusion Network for Efficient Video RecognitionCode1
A Comprehensive Study of Deep Video Action RecognitionCode1
ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency PredictionCode1
Spatial Temporal Transformer Network for Skeleton-based Action RecognitionCode1
Interactive Fusion of Multi-level Features for Compositional Activity RecognitionCode1
VideoMix: Rethinking Data Augmentation for Video ClassificationCode1
Fast Fourier ConvolutionCode1
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video ClassificationCode1
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Play Fair: Frame Attributions in Video ModelsCode1
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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