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 1001–1050 of 1477 papers

TitleStatusHype
Rethinking the Faster R-CNN Architecture for Temporal Action Localization—0
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities—0
Precise Temporal Action Localization by Evolving Temporal Proposals—0
STAIR Actions: A Video Dataset of Everyday Home ActionsCode0
Audio-Visual Scene Analysis with Self-Supervised Multisensory FeaturesCode0
DIY Human Action Data Set Generation—0
Non-Linear Temporal Subspace Representations for Activity Recognition—0
Video Representation Learning Using Discriminative Pooling—0
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos—0
Towards Universal Representation for Unseen Action Recognition—0
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks—0
Exploiting deep residual networks for human action recognition from skeletal data—0
T-RECS: Training for Rate-Invariant Embeddings by Controlling Speed for Action Recognition—0
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition—0
Deja Vu: Motion Prediction in Static ImagesCode0
Featureless: Bypassing feature extraction in action categorization—0
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks—0
Analysis of Hand Segmentation in the WildCode0
Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition—0
2D/3D Pose Estimation and Action Recognition using Multitask Deep LearningCode0
Real-Time End-to-End Action Detection with Two-Stream Networks—0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Learning Representative Temporal Features for Action Recognition—0
Fisherposes for Human Action Recognition Using Kinect Sensor DataCode0
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices—0
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences—0
Histogram of Oriented Depth Gradients for Action Recognition—0
A Generative Approach to Zero-Shot and Few-Shot Action Recognition—0
Let's Dance: Learning From Online Dance VideosCode0
DiscrimNet: Semi-Supervised Action Recognition from Videos using Generative Adversarial Networks—0
Semi-supervised Fisher vector network—0
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition—0
Moments in Time Dataset: one million videos for event understandingCode0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
What have we learned from deep representations for action recognition?—0
Distributed non-parametric deep and wide networks—0
A Unified Method for First and Third Person Action Recognition—0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
On the Integration of Optical Flow and Action Recognition—0
Human Action Recognition: Pose-based Attention draws focus to Hands—0
Weakly Supervised Action Localization by Sparse Temporal Pooling NetworkCode0
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition—0
Deception Detection in Videos—0
Im2Flow: Motion Hallucination from Static Images for Action RecognitionCode0
Cooperative Training of Deep Aggregation Networks for RGB-D Action Recognition—0
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions—0
Multimodal Visual Concept Learning with Weakly Supervised TechniquesCode0
Compressed Video Action RecognitionCode0
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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