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 21512175 of 2759 papers

TitleStatusHype
Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions0
MV2MAE: Multi-View Video Masked Autoencoders0
MV-GMN: State Space Model for Multi-View Action Recognition0
MVHumanNet: A Large-scale Dataset of Multi-view Daily Dressing Human Captures0
MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition0
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation0
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition0
Natural Language Descriptions for Human Activities in Video Streams0
Neural Graph Matching Networks for Fewshot 3D Action Recognition0
Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition0
No-audio speaking status detection in crowded settings via visual pose-based filtering and wearable acceleration0
Noise-Tolerant Learning for Audio-Visual Action Recognition0
Non-Linear Temporal Subspace Representations for Activity Recognition0
Non-local Recurrent Neural Memory for Supervised Sequence Modeling0
Normalized Human Pose Features for Human Action Video Alignment0
Nrityantar: Pose oblivious Indian classical dance sequence classification system0
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition0
Nuisance-Label Supervision: Robustness Improvement by Free Labels0
NUTA: Non-uniform Temporal Aggregation for Action Recognition0
Nymeria: A Massive Collection of Multimodal Egocentric Daily Motion in the Wild0
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition0
Object Activity Scene Description, Construction and Recognition0
Object-based (yet Class-agnostic) Video Domain Adaptation0
ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition0
Object Properties Inferring from and Transfer for Human Interaction Motions0
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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