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 11511200 of 2759 papers

TitleStatusHype
An Analysis of Deep Neural Networks with Attention for Action Recognition from a Neurophysiological Perspective0
Fine-grained activity recognition for assembly videos0
Class-Incremental Learning for Action Recognition in Videos0
Fine-Grain Annotation of Cricket Videos0
Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action0
An Analysis of Action Recognition Datasets for Language and Vision Tasks0
A Comprehensive Review of Few-shot Action Recognition0
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models0
FILS: Self-Supervised Video Feature Prediction In Semantic Language Space0
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints0
Few-Shot Video Classification via Temporal Alignment0
Classifying action correctness in physical rehabilitation exercises0
Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks0
Few Shot Activity Recognition Using Variational Inference0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms0
Action Recognition in Video Recordings from Gynecologic Laparoscopy0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport0
Few-shot Action Recognition with Captioning Foundation Models0
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization0
Chop & Learn: Recognizing and Generating Object-State Compositions0
FenceNet: Fine-grained Footwork Recognition in Fencing0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition0
ChildPlay-Hand: A Dataset of Hand Manipulations in the Wild0
Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework0
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning0
Federated Action Recognition on Heterogeneous Embedded Devices0
Feature-Supervised Action Modality Transfer0
Feature Sampling Strategies for Action Recognition0
Channel-Temporal Attention for First-Person Video Domain Adaptation0
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets0
Featureless: Bypassing feature extraction in action categorization0
CHAM: action recognition using convolutional hierarchical attention model0
Feature Hallucination for Self-supervised Action Recognition0
Challenges of the Creation of a Dataset for Vision Based Human Hand Action Recognition in Industrial Assembly0
An Action Recognition network for specific target based on rMC and RPN0
Action Recognition in the Frequency Domain0
Feature and Region Selection for Visual Learning0
FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge0
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge0
Challenge report:VIPriors Action Recognition Challenge0
Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning0
Fast, invariant representation for human action in the visual system0
Action recognition in still images by latent superpixel classification0
A compact sequence encoding scheme for online human activity recognition in HRI applications0
FASTER Recurrent Networks for Efficient Video Classification0
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition0
FACTS: Fine-Grained Action Classification for Tactical Sports0
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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