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 1401–1450 of 1477 papers

TitleStatusHype
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
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
Hollywood 3D: Recognizing Actions in 3D Natural Scenes—0
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding—0
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition—0
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks—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
Human Action Recognition Based on Multi-scale Feature Maps from Depth Video Sequences—0
Human Action Recognition Based on Spatial-Temporal Attention—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
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
Human action recognition with a large-scale brain-inspired photonic computer—0
Human Action Recognition with Deep Temporal Pyramids—0
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks—0
Human Action Recognition without Human—0
Understanding More about Human and Machine Attention in Deep Neural Networks—0
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition—0
Hyper-Fisher Vectors for Action Recognition—0
iCub! Do you recognize what I am doing?: multimodal human action recognition on multisensory-enabled iCub robot—0
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition—0
Image and Video Mining through Online Learning—0
Image-based OoD-Detector Principles on Graph-based Input Data in Human Action Recognition—0
Improved Dense Trajectory with Cross Streams—0
Improve Temporal Action Proposals using Hierarchical Context—0
Improving Action Localization by Progressive Cross-stream Cooperation—0
Improving Continuous Sign Language Recognition: Speech Recognition Techniques and System Design—0
Improving Human Action Recognition by Non-action Classification—0
Improving Interpretability of Deep Neural Networks with Semantic Information—0
Improving performance of recurrent neural network with relu nonlinearity—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