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
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization—0
Actionness Estimation Using Hybrid Fully Convolutional Networks—0
DL-SFA: Deeply-Learned Slow Feature Analysis for Action Recognition—0
DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the Dark—0
BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications—0
DIY Human Action Data Set Generation—0
Dividing and Aggregating Network for Multi-view Action Recognition—0
Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections—0
Divide and Conquer for Single-Frame Temporal Action Localization—0
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition—0
Distributed non-parametric deep and wide networks—0
DistInit: Learning Video Representations Without a Single Labeled Video—0
AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Actionness-Assisted Recognition of Actions—0
Accuracy and Performance Comparison of Video Action Recognition Approaches—0
Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization—0
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition—0
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks—0
Discriminatively Trained Latent Ordinal Model for Video Classification—0
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition—0
Discriminative Hierarchical Rank Pooling for Activity Recognition—0
Discriminative convolutional Fisher vector network for action recognition—0
A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition—0
Discriminative Bayesian Dictionary Learning for Classification—0
Direct Dense Pose Estimation—0
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition—0
Action Machine: Rethinking Action Recognition in Trimmed Videos—0
Differential Recurrent Neural Networks for Action Recognition—0
Automatic Curation of Golf Highlights using Multimodal Excitement Features—0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition—0
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution—0
DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting—0
AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos—0
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization—0
A Cause and Effect Analysis of Motion Trajectories for Modeling Actions—0
Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych—0
Detection of Manipulation Action Consequences (MAC)—0
Detecting Hands in Egocentric Videos: Towards Action Recognition—0
A Unified Method for First and Third Person Action Recognition—0
Describing Video Contents in Natural Language—0
Depthwise Spatio-Temporal STFT Convolutional Neural Networks for Human Action Recognition—0
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks—0
Actor-Centric Relation Network—0
Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras—0
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition—0
Density-Guided Label Smoothing for Temporal Localization of Driving Actions—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—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