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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1050 of 1477 papers

TitleStatusHype
A^2-Nets: Double Attention Networks—0
A3D: Adaptive 3D Networks for Video Action Recognition—0
A baseline on continual learning methods for video action recognition—0
A Cause and Effect Analysis of Motion Trajectories for Modeling Actions—0
Accuracy and Performance Comparison of Video Action Recognition Approaches—0
A Compact Kernel Approximation for 3D Action Recognition—0
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization—0
ActAR: Actor-Driven Pose Embeddings for Video Action Recognition—0
Action2Activity: Recognizing Complex Activities from Sensor Data—0
Action2Vec: A Crossmodal Embedding Approach to Action Learning—0
Action4D: Online Action Recognition in the Crowd and Clutter—0
Action4D: Real-time Action Recognition in the Crowd and Clutter—0
Action-Attending Graphic Neural Network—0
Action Capsules: Human Skeleton Action Recognition—0
Action Classification via Concepts and Attributes—0
Action Class Relation Detection and Classification Across Multiple Video Datasets—0
Action Detail Matters: Refining Video Recognition with Local Action Queries—0
Action Detection by Implicit Intentional Motion Clustering—0
ActionFlowNet: Learning Motion Representation for Action Recognition—0
Action in Mind: A Neural Network Approach to Action Recognition and Segmentation—0
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization—0
Action Machine: Rethinking Action Recognition in Trimmed Videos—0
Actionness-Assisted Recognition of Actions—0
Actionness Estimation Using Hybrid Fully Convolutional Networks—0
Action Recognition Based on Joint Trajectory Maps with Convolutional Neural Networks—0
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks—0
Action Recognition based on Subdivision-Fusion Model—0
Action Recognition by Hierarchical Mid-level Action Elements—0
Action Recognition by Hierarchical Sequence Summarization—0
Action recognition by learning pose representations—0
Action recognition from depth maps using deep convolutional neural networks—0
Action Recognition: From Static Datasets to Moving Robots—0
Action recognition in real-world videos—0
Action recognition in still images by latent superpixel classification—0
Action Recognition in the Frequency Domain—0
Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework—0
Action Recognition in Video Using Sparse Coding and Relative Features—0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision—0
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion—0
Action Recognition with Image Based CNN Features—0
Action Recognition with Joint Attention on Multi-Level Deep Features—0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences—0
Action Representation Using Classifier Decision Boundaries—0
Action Sensitivity Learning for Temporal Action Localization—0
Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition—0
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
Action Understanding with Multiple Classes of Actors—0
Action Unit Memory Network for Weakly Supervised Temporal Action Localization—0
ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition—0
Active Generation Network of Human Skeleton for Action Recognition—0
Show:102550
← PrevPage 21 of 30Next →
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