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 276300 of 2759 papers

TitleStatusHype
FLAVR: Flow-Agnostic Video Representations for Fast Frame InterpolationCode1
Approximated Bilinear Modules for Temporal ModelingCode1
Actions as Moving PointsCode1
Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal DetectionCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
AutoLabel: CLIP-based framework for Open-set Video Domain AdaptationCode1
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion RecognitionCode1
Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic ScenesCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
BEVT: BERT Pretraining of Video TransformersCode1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse DomainsCode1
ARID: A New Dataset for Recognizing Action in the DarkCode1
AR-Net: Adaptive Frame Resolution for Efficient Action RecognitionCode1
ViViT: A Video Vision TransformerCode1
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIPCode1
ArtEmis: Affective Language for Visual ArtCode1
Bridging Video-text Retrieval with Multiple Choice QuestionsCode1
Bringing Online Egocentric Action Recognition into the wildCode1
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular DiseaseCode1
C2C: Component-to-Composition Learning for Zero-Shot Compositional Action RecognitionCode1
Explore Human Parsing Modality for Action RecognitionCode1
CAST: Cross-Attention in Space and Time for Video Action RecognitionCode1
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
EZ-CLIP: Efficient Zeroshot Video Action RecognitionCode1
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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