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 501525 of 2759 papers

TitleStatusHype
TAM: Temporal Adaptive Module for Video RecognitionCode1
Project RISE: Recognizing Industrial Smoke EmissionsCode1
3DV: 3D Dynamic Voxel for Action Recognition in Depth VideoCode1
Rolling-Unrolling LSTMs for Action Anticipation from First-Person VideoCode1
Audio-Visual Instance Discrimination with Cross-Modal AgreementCode1
SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action RecognitionCode1
SpeedNet: Learning the Speediness in VideosCode1
Improved Residual Networks for Image and Video RecognitionCode1
Temporal Pyramid Network for Action RecognitionCode1
A Local-to-Global Approach to Multi-modal Movie Scene SegmentationCode1
Deep Multimodal Feature Encoding for Video OrderingCode1
TEA: Temporal Excitation and Aggregation for Action RecognitionCode1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D NetworksCode1
Temporally Coherent Embeddings for Self-Supervised Video Representation LearningCode1
Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action RecognitionCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
Top-1 Solution of Multi-Moments in Time Challenge 2019Code1
On Compositions of Transformations in Contrastive Self-Supervised LearningCode1
Infrared and 3D skeleton feature fusion for RGB-D action recognitionCode1
Vision-based Fight Detection from Surveillance CamerasCode1
Multi-Modal Domain Adaptation for Fine-Grained Action RecognitionCode1
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action RecognitionCode1
Learning Spatiotemporal Features via Video and Text Pair DiscriminationCode1
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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