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 1301–1350 of 1477 papers

TitleStatusHype
Emotion-Based Crowd Representation for Abnormality Detection—0
Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream—0
End-to-End Joint Semantic Segmentation of Actors and Actions in Video—0
Enhanced skeleton visualization for view invariant human action recognition—0
Enhanced Spatiotemporal Prediction Using Physical-guided And Frequency-enhanced Recurrent Neural Networks—0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
Ensembles of Deep Neural Networks for Action Recognition in Still Images—0
Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization—0
Evaluating the Effectiveness of Automated Identity Masking (AIM) Methods with Human Perception and a Deep Convolutional Neural Network (CNN)—0
Evaluating Transformers for Lightweight Action Recognition—0
Evaluation of Color STIPs for Human Action Recognition—0
Event-based Action Recognition Using Timestamp Image Encoding Network—0
Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation—0
Evolution-Preserving Dense Trajectory Descriptors—0
EXMOVES: Classifier-based Features for Scalable Action Recognition—0
Expanded Parts Model for Human Attribute and Action Recognition in Still Images—0
Exploiting deep residual networks for human action recognition from skeletal data—0
Exploiting Language Models for Visual Recognition—0
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition—0
Exploiting Structure Sparsity for Covariance-based Visual Representation—0
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks—0
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
Exploring Explainability in Video Action Recognition—0
Exploring Feature Representation and Training strategies in Temporal Action Localization—0
Exploring Frame Segmentation Networks for Temporal Action Localization—0
Exploring Stronger Feature for Temporal Action Localization—0
Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos—0
Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos—0
Exploring Temporal Preservation Networks for Precise Temporal Action Localization—0
Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)—0
Extending Temporal Data Augmentation for Video Action Recognition—0
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds—0
F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning—0
Face-space Action Recognition by Face-Object Interactions—0
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos—0
Feature and Region Selection for Visual Learning—0
Featureless: Bypassing feature extraction in action categorization—0
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets—0
Feature Sampling Strategies for Action Recognition—0
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization—0
Fine-Grain Annotation of Cricket Videos—0
First Person Action Recognition Using Deep Learned Descriptors—0
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos—0
Flatten: Video Action Recognition is an Image Classification task—0
Flip-Invariant Motion Representation—0
Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition—0
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition—0
Force From Motion: Decoding Physical Sensation in a First Person Video—0
Show:102550
← PrevPage 27 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