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 701–750 of 1477 papers

TitleStatusHype
Real-time Spatio-temporal Action Localization via Learning Motion Representation—0
Recognize Actions by Disentangling Components of Dynamics—0
Recognizing Activities by Attribute Dynamics—0
Recognizing Human Actions as the Evolution of Pose Estimation Maps—0
Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning—0
Reconstruction-free action inference from compressive imagers—0
Recurrent Residual Learning for Action Recognition—0
Recurring the Transformer for Video Action Recognition—0
Recursive Frechet Mean Computation on the Grassmannian and its Applications to Computer Vision—0
ReGen: A good Generative Zero-Shot Video Classifier Should be Rewarded—0
REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition—0
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition—0
Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition—0
Relational Long Short-Term Memory for Video Action Recognition—0
Relation Modeling in Spatio-Temporal Action Localization—0
Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Residual Frames with Efficient Pseudo-3D CNN for Human Action Recognition—0
RESOUND: Towards Action Recognition without Representation Bias—0
REST: REtrieve & Self-Train for generative action recognition—0
Rethinking Full Connectivity in Recurrent Neural Networks—0
Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction—0
Rethinking the Faster R-CNN Architecture for Temporal Action Localization—0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
Revisiting Human Action Recognition: Personalization vs. Generalization—0
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding—0
RGB-D-based Action Recognition Datasets: A Survey—0
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks—0
RGB Video Based Tennis Action Recognition Using a Deep Historical Long Short-Term Memory—0
RNN Fisher Vectors for Action Recognition and Image Annotation—0
RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem—0
RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model—0
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions—0
Robust Estimation of 3D Human Poses from a Single Image—0
Robust features for facial action recognition—0
Robust Multi-body Feature Tracker: A Segmentation-free Approach—0
Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos—0
Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data—0
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition—0
R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition—0
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021—0
Sampling Strategies for Real-Time Action Recognition—0
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition—0
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines—0
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition—0
Scale Matters: Temporal Scale Aggregation Network for Precise Action Localization in Untrimmed Videos—0
Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks—0
Scenes-Objects-Actions: A Multi-Task, Multi-Label Video Dataset—0
SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition—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