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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2301–2350 of 2759 papers

TitleStatusHype
Face-space Action Recognition by Face-Object Interactions—0
FACTS: Fine-Grained Action Classification for Tactical Sports—0
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain—0
FASTER Recurrent Networks for Efficient Video Classification—0
Fast, invariant representation for human action in the visual system—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos—0
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge—0
FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge—0
Feature and Region Selection for Visual Learning—0
Feature Hallucination for Self-supervised Action Recognition—0
Featureless: Bypassing feature extraction in action categorization—0
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets—0
Feature Sampling Strategies for Action Recognition—0
Feature-Supervised Action Modality Transfer—0
Federated Action Recognition on Heterogeneous Embedded Devices—0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
FenceNet: Fine-grained Footwork Recognition in Fencing—0
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization—0
Few-shot Action Recognition with Captioning Foundation Models—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
Few Shot Activity Recognition Using Variational Inference—0
Few-Shot Video Classification via Temporal Alignment—0
FILS: Self-Supervised Video Feature Prediction In Semantic Language Space—0
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models—0
Fine-Grain Annotation of Cricket Videos—0
Fine-grained activity recognition for assembly videos—0
Fine-grained Multi-Modal Self-Supervised Learning—0
Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition—0
Fine-grained Video Categorization with Redundancy Reduction Attention—0
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding—0
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition—0
First Person Action Recognition Using Deep Learned Descriptors—0
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos—0
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition—0
Flatten: Video Action Recognition is an Image Classification task—0
Flip-Invariant Motion Representation—0
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition—0
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition—0
Flow Dynamics Correction for Action Recognition—0
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition—0
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition—0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition—0
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition—0
Force From Motion: Decoding Physical Sensation in a First Person Video—0
ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection—0
Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras—0
fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs—0
Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition—0
Show:102550
← PrevPage 47 of 56Next →
All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4InternVideoTop-1 Accuracy77.2—Unverified
5DejaVidTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3OmniVec3-fold Accuracy99.6—Unverified
4VideoMAE V2-g3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
10Text4Vis3-fold Accuracy98.2—Unverified