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 601–650 of 1477 papers

TitleStatusHype
HAUAR: Home Automation Using Action Recognition—0
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos—0
Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020—0
Heterogeneous Skeleton-Based Action Representation Learning—0
Graph Convolutional Module for Temporal Action Localization in Videos—0
IndGIC: Supervised Action Recognition under Low Illumination—0
Inertial Sensor Data To Image Encoding For Human Action Recognition—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
Context-based Object Viewpoint Estimation: A 2D Relational Approach—0
Hierarchical Feature Aggregation Networks for Video Action Recognition—0
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition—0
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition—0
Hierarchical Multi-scale Attention Networks for Action Recognition—0
Hierarchical Spatial Sum-Product Networks for Action Recognition in Still Images—0
Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition—0
Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine—0
Continuous Action Recognition Based on Sequence Alignment—0
High Speed Human Action Recognition using a Photonic Reservoir Computer—0
Histogram of Oriented Depth Gradients for Action Recognition—0
Histogram of Oriented Principal Components for Cross-View Action Recognition—0
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions—0
Gradient Frequency Modulation for Visually Explaining Video Understanding Models—0
Hollywood 3D: Recognizing Actions in 3D Natural Scenes—0
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding—0
Action Recognition in Video Using Sparse Coding and Relative Features—0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks—0
Gradient Boundary Histograms for Action Recognition—0
HTNet: Anchor-free Temporal Action Localization with Hierarchical Transformers—0
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model—0
Human Action Attribute Learning From Video Data Using Low-Rank Representations—0
Human Action Forecasting by Learning Task Grammars—0
Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention Model—0
Human Action Recognition Across Datasets by Foreground-weighted Histogram Decomposition—0
Human Action Recognition and Assessment via Deep Neural Network Self-Organization—0
Human Action Recognition and Prediction: A Survey—0
Human Action Recognition Based on Context-Dependent Graph Kernels—0
Combining Spatio-Temporal Appearance Descriptors and Optical Flow for Human Action Recognition in Video Data—0
Human Action Recognition Based on Spatial-Temporal Attention—0
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization—0
Human Action Recognition from Various Data Modalities: A Review—0
Human Action Recognition (HAR) Using Skeleton-based Spatial Temporal Relative Transformer Network: ST-RTR—0
Human Action Recognition in Drone Videos using a Few Aerial Training Examples—0
Human Action Recognition in Egocentric Perspective Using 2D Object and Hands Pose—0
Human Action Recognition in Still Images Using ConViT—0
Human Action Recognition: Pose-based Attention draws focus to Hands—0
Human Action Recognition System using Good Features and Multilayer Perceptron Network—0
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition—0
Human Action Recognition using Factorized Spatio-Temporal Convolutional Networks—0
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines—0
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—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