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 27012725 of 2759 papers

TitleStatusHype
Deja Vu: Motion Prediction in Static ImagesCode0
DejaVid: Encoder-Agnostic Learned Temporal Matching for Video ClassificationCode0
Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie StimuliCode0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Two-Stream Convolutional Networks for Action Recognition in VideosCode0
Deep Point-wise Prediction for Action Temporal ProposalCode0
DeepGRU: Deep Gesture Recognition UtilityCode0
DEAR: Depth-Enhanced Action RecognitionCode0
TEAM-Net: Multi-modal Learning for Video Action Recognition with Partial DecodingCode0
Two-stream Flow-guided Convolutional Attention Networks for Action RecognitionCode0
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNNCode0
ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action RecognitionCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
Video-based cattle identification and action recognitionCode0
Seeing and Hearing Egocentric Actions: How Much Can We Learn?Code0
DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action RecognitionCode0
Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with AutismCode0
From Recognition to Prediction: Leveraging Sequence Reasoning for Action AnticipationCode0
Growing a Brain with Sparsity-Inducing Generation for Continual LearningCode0
D3D: Distilled 3D Networks for Video Action RecognitionCode0
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
Telling Stories for Common Sense Zero-Shot Action RecognitionCode0
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action DetectionCode0
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNetCode0
Selective Volume Mixup for Video Action RecognitionCode0
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified