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

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Showing 1151–1200 of 2759 papers

TitleStatusHype
An Analysis of Deep Neural Networks with Attention for Action Recognition from a Neurophysiological Perspective—0
Fine-grained activity recognition for assembly videos—0
Class-Incremental Learning for Action Recognition in Videos—0
Fine-Grain Annotation of Cricket Videos—0
Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action—0
An Analysis of Action Recognition Datasets for Language and Vision Tasks—0
A Comprehensive Review of Few-shot Action Recognition—0
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models—0
FILS: Self-Supervised Video Feature Prediction In Semantic Language Space—0
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints—0
Few-Shot Video Classification via Temporal Alignment—0
Classifying action correctness in physical rehabilitation exercises—0
Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks—0
Few Shot Activity Recognition Using Variational Inference—0
Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Action Recognition in Video Recordings from Gynecologic Laparoscopy—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
Few-shot Action Recognition with Captioning Foundation Models—0
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization—0
Chop & Learn: Recognizing and Generating Object-State Compositions—0
FenceNet: Fine-grained Footwork Recognition in Fencing—0
Feedback Graph Convolutional Network for Skeleton-based Action Recognition—0
ChildPlay-Hand: A Dataset of Hand Manipulations in the Wild—0
Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework—0
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning—0
Federated Action Recognition on Heterogeneous Embedded Devices—0
Feature-Supervised Action Modality Transfer—0
Feature Sampling Strategies for Action Recognition—0
Channel-Temporal Attention for First-Person Video Domain Adaptation—0
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets—0
Featureless: Bypassing feature extraction in action categorization—0
CHAM: action recognition using convolutional hierarchical attention model—0
Feature Hallucination for Self-supervised Action Recognition—0
Challenges of the Creation of a Dataset for Vision Based Human Hand Action Recognition in Industrial Assembly—0
An Action Recognition network for specific target based on rMC and RPN—0
Action Recognition in the Frequency Domain—0
Feature and Region Selection for Visual Learning—0
FBK-HUPBA Submission to the EPIC-Kitchens Action Recognition 2020 Challenge—0
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge—0
Challenge report:VIPriors Action Recognition Challenge—0
Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
Fast, invariant representation for human action in the visual system—0
Action recognition in still images by latent superpixel classification—0
A compact sequence encoding scheme for online human activity recognition in HRI applications—0
FASTER Recurrent Networks for Efficient Video Classification—0
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain—0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition—0
FACTS: Fine-Grained Action Classification for Tactical Sports—0
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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
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-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
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified