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 1201–1250 of 1477 papers

TitleStatusHype
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model—0
Action Recognition Based on Optimal Joint Selection and Discriminative Depth DescriptorCode0
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation—0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Learning Multi-level Features For Sensor-based Human Action Recognition—0
Self-Supervised Video Representation Learning With Odd-One-Out Networks—0
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data—0
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation—0
Joint Network based Attention for Action Recognition—0
Learning long-term dependencies for action recognition with a biologically-inspired deep networkCode0
Learning To Score Olympic EventsCode0
Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks—0
Action2Activity: Recognizing Complex Activities from Sensor Data—0
Convolutional Neural Network Language ModelsCode0
Sliding Dictionary Based Sparse Representation For Action Recognition—0
Exploiting Structure Sparsity for Covariance-based Visual Representation—0
Review of Action Recognition and Detection MethodsCode0
ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration—0
Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions—0
Real-time Human Pose Estimation from Video with Convolutional Neural Networks—0
Pose from Action: Unsupervised Learning of Pose Features based on Motion—0
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups—0
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—0
Image and Video Mining through Online Learning—0
Making a Case for Learning Motion Representations with Phase—0
Human Action Recognition without Human—0
Spatio-temporal Aware Non-negative Component Representation for Action Recognition—0
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition—0
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods—0
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition—0
Discriminatively Trained Latent Ordinal Model for Video Classification—0
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
A Survey of Visual Analysis of Human Motion and Its Applications—0
Dynamic Probabilistic Network Based Human Action Recognition—0
Emotion-Based Crowd Representation for Abnormality Detection—0
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition—0
Kinematic-Layout-aware Random Forests for Depth-based Action Recognition—0
Hierarchical Attention Network for Action Recognition in Videos—0
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking—0
Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection—0
Annotation Methodologies for Vision and Language Dataset Creation—0
Action Recognition with Joint Attention on Multi-Level Deep Features—0
Zero-Shot Visual Recognition via Bidirectional Latent Embedding—0
VideoLSTM Convolves, Attends and Flows for Action RecognitionCode0
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data—0
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets—0
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform—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