SOTAVerified

Temporal Action Localization

Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.

Papers

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Showing 1401–1450 of 1477 papers

TitleStatusHype
Submodular Attribute Selection for Action Recognition in Video—0
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition—0
A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis—0
A Latent Clothing Attribute Approach for Human Pose Estimation—0
Histogram of Oriented Principal Components for Cross-View Action Recognition—0
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation—0
Action Recognition in the Frequency Domain—0
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences—0
Temporal Extension of Scale Pyramid and Spatial Pyramid Matching for Action Recognition—0
Skeletal quads: Human action recognition using joint quadruples—0
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos—0
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition—0
TUHOI: Trento Universal Human Object Interaction Dataset—0
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset—0
Feature and Region Selection for Visual Learning—0
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
Robust Estimation of 3D Human Poses from a Single Image—0
Log-Euclidean Bag of Words for Human Action Recognition—0
Two-Stream Convolutional Networks for Action Recognition in VideosCode0
Continuous Action Recognition Based on Sequence Alignment—0
Human Action Recognition Based on Context-Dependent Graph Kernels—0
Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition—0
Multi-View Super Vector for Action Recognition—0
Efficient Feature Extraction, Encoding and Classification for Action Recognition—0
Efficient Nonlinear Markov Models for Human Motion—0
A Cause and Effect Analysis of Motion Trajectories for Modeling Actions—0
Simplex-Based 3D Spatio-Temporal Feature Description for Action Recognition—0
Towards Good Practices for Action Video Encoding—0
Range-Sample Depth Feature for Action Recognition—0
DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition—0
3D Pose from Motion for Cross-view Action Recognition via Non-linear Circulant Temporal EncodingCode0
Efficient Action Localization with Approximately Normalized Fisher Vectors—0
Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition—0
Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context—0
Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras—0
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets—0
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice—0
Cross-view Action Modeling, Learning and Recognition—0
From Synsets to Videos: Enriching ItalWordNet Multimodally—0
Shrinkage Optimized Directed Information using Pictorial Structures for Action Recognition—0
KPCA Spatio-temporal trajectory point cloud classifier for recognizing human actions in a CBVR system—0
Bregman Divergences for Infinite Dimensional Covariance Matrices—0
Multiview Hessian regularized logistic regression for action recognition—0
Group sparsity and geometry constrained dictionary learning for action recognition from depth maps.—0
A Hybrid Loss for Multiclass and Structured Prediction—0
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds—0
Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition—0
EXMOVES: Classifier-based Features for Scalable Action Recognition—0
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization—0
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All datasetsTHUMOS14ActivityNet-1.3HACSFineActionMultiTHUMOSCrossTaskEPIC-KITCHENS-100MUSESActivityNet-1.2Ego4D MQ testEgo4D MQ valMEXaction2

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AdaTAD (VideoMAEv2-giant)Avg mAP (0.3:0.7)76.9—Unverified
2RDFA-S6 (InternVideo2-6B)Avg mAP (0.3:0.7)74.2—Unverified
3ActionMamba(InternVideo2-6B)Avg mAP (0.3:0.7)72.72—Unverified
4GCMmAP IOU@0.172.5—Unverified
5AGT (Ours)mAP IOU@0.172.1—Unverified
6InternVideo2-6BAvg mAP (0.3:0.7)72—Unverified
7ActionFormer (InternVideo features)Avg mAP (0.3:0.7)71.58—Unverified
8TriDet (VideoMAE v2-g feature)Avg mAP (0.3:0.7)70.1—Unverified
9InternVideo2-1BAvg mAP (0.3:0.7)69.8—Unverified
10ActionFormer (VideoMAE V2-g features)Avg mAP (0.3:0.7)69.6—Unverified
#ModelMetricClaimedVerifiedStatus
1UnLoc-LmAP IOU@0.559.3—Unverified
2RDFA-S6 (InternVideo2-6B)mAP42.9—Unverified
3ActionMamba (InternVideo2-6B)mAP42.02—Unverified
4PRN+BMN (ensemble)mAP42—Unverified
5AdaTAD (VideoMAEv2-giant)mAP41.93—Unverified
6InternVideo2-6BmAP41.2—Unverified
7InternVideo2-1BmAP40.4—Unverified
8UniMD+Sync.mAP39.83—Unverified
9PRN (CSN)mAP39.4—Unverified
10InternVideomAP39—Unverified
#ModelMetricClaimedVerifiedStatus
1RDFA-S6 (InternVideo2-6B)Average-mAP45.8—Unverified
2ActionMamba(InternVideo2-6B)Average-mAP44.56—Unverified
3DyFADet(VideoMAEv2)Average-mAP44.3—Unverified
4InternVideo2-6BAverage-mAP43.3—Unverified
5TriDet (VideoMAEv2)Average-mAP43.1—Unverified
6InternVideo2-1BAverage-mAP42.4—Unverified
7InternVideoAverage-mAP41.55—Unverified
8TriDet (SlowFast)Average-mAP38.6—Unverified
9TriDet (I3D RGB)Average-mAP36.8—Unverified
10TadTr (I3D RGB)Average-mAP32.09—Unverified
#ModelMetricClaimedVerifiedStatus
1RDFA-S6 (InternVideo2-6B)mAP29.6—Unverified
2ActionMamba(InternVideo2-6B)mAP29.04—Unverified
3InternVideo2-6BmAP27.7—Unverified
4DyFADet (VideoMAE v2-g)mAP23.8—Unverified
5VideoMAE V2-gmAP18.24—Unverified
6InternVideomAP17.57—Unverified
7BMN (i3d feaure)mAP9.25—Unverified
8G-TAD (i3d feature)mAP9.06—Unverified
9DBG (i3d feature)mAP6.75—Unverified
#ModelMetricClaimedVerifiedStatus
1TriDet (VideoMAEv2)Average mAP37.5—Unverified
2DualDETR (I3D-rgb)Average mAP32.64—Unverified
3TriDet (I3D-rgb)Average mAP30.7—Unverified
4TemporalMaxerAverage mAP29.9—Unverified
5PointTADAverage mAP23.5—Unverified
6PDANAverage mAP17.3—Unverified
7MS-TCTAverage mAP16.2—Unverified
8MLADAverage mAP14.2—Unverified
#ModelMetricClaimedVerifiedStatus
1VideoCLIPRecall47.3—Unverified
2VLMRecall46.5—Unverified
3TACoRecall42.5—Unverified
4Text-Video EmbeddingRecall33.6—Unverified
5Fully-supervised upper-boundRecall31.6—Unverified
6ZhukovRecall22.4—Unverified
7AlayracRecall13.3—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaTAD (verb, VideoMAE-L)Avg mAP (0.1-0.5)29.3—Unverified
2TriDet (verb)Avg mAP (0.1-0.5)25.4—Unverified
3TemporalMaxer (verb)Avg mAP (0.1-0.5)24.5—Unverified
4ActionFormer (verb)Avg mAP (0.1-0.5)23.5—Unverified
5G-TAD (verb)Avg mAP (0.1-0.5)9.4—Unverified
6BMN (verb)Avg mAP (0.1-0.5)8.4—Unverified
#ModelMetricClaimedVerifiedStatus
1TemporalMaxermAP27.2—Unverified
2MUSESmAP18.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepMetricLearnermAP IOU@0.535.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ActionFormer (SlowFast+Omnivore+EgoVLP)Average mAP21.76—Unverified
#ModelMetricClaimedVerifiedStatus
1ActionFormer (SlowFast+Omnivore+EgoVLP)Average mAP21.4—Unverified
#ModelMetricClaimedVerifiedStatus
1S-CNNmAP7.4—Unverified