- Constraining Temporal Relationship for Action Localization (arxiv 2020)
- (AGCN-P-3DCNNs) Graph Attention Based Proposal 3D ConvNets for Action Detection (AAAI20)
- (PBRNet) Progressive Boundary Refinement Network for Temporal Action Detection (AAAI20)
- (RapNet) Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network (AAAI20)
- (S-2D-TAN) Learning Sparse 2D Temporal Adjacent Networks for Temporal Action Localization (HACS Temporal Action Localization Challenge at ICCV 2019)
- (G-TAD) G-TAD: Sub-Graph Localization for Temporal Action Detection (CVPR 2020) CODE
- (CMSN) CMSN: Continuous Multi-stage Network and Variable Margin Cosine Loss for Temporal Action Proposal Generation (arxiv 2019)
- (DBG) Fast Learning of Temporal Action Proposal via Dense Boundary Generator (AAAI 2020) CODE
- (AFO-TAD) AFO-TAD: Anchor-free One-Stage Detector for Temporal Action Detection (arxiv 2019)
- (semi-supervised) Learning Temporal Action Proposals With Fewer Labels (ICCV 2019)
- (DPP.AnchorFree) Deep Point-wise Prediction for Action Temporal Proposal (ICONIP 2019)
- (P-GCN) Graph Convolutional Networks for Temporal Action Localization (ICCV 2019) CODE.pytorch
- (C-TCN) Deep Concept-wise Temporal Convolutional Networks for Action Localization (arxiv 2019) CODE.PadddlePaddle
- (TSANet) Scale Matters: Temporal Scale Aggregation Network for Precise Action Localization in Untrimmed Videos (ICME2020)
- (BMN) BMN: Boundary-Matching Network for Temporal Action Proposal Generation (ICCV 2019)
- (TGM) Temporal Gaussian Mixture Layer for Videos (ICML 2019) CODE.pytorch
- (MGG) Multi-granularity Generator for Temporal Action Proposal (CVPR 2019)
- (GTAN) Gaussian Temporal Awareness Networks for Action Localization (CVPR 2019)
- Relational Prototypical Network for Weakly Supervised Temporal Action Localization (AAAI20)
- (BaSNet) Background Suppression Network for Weakly-supervised Temporal Action Localization (AAAI20)CODE.pytorch
- (LPAT) LPAT: Learning to Predict Adaptive Threshold for Weakly-supervised Temporal Action Localization (arxiv 2019)
- (3C-Net) 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization (ICCV2019)CODE.pytorch
- (TSM) Temporal Structure Mining for Weakly Supervised Action Detection (ICCV2019)
- (CleanNet) Weakly Supervised Temporal Action Localization through Contrast based Evaluation Networks (ICCV2019)
- (BM) Weakly-supervised Action Localization with Background Modeling (ICCV 2019)
- (ASSG) Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization (ACM MM19)
- (CMCS) Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization (CVPR19)CODE.pytorch
- Weakly-Supervised Temporal Localization via Occurrence Count Learning (ICML 2019)
- (MAAN) Marginalized Average Attentional Network for Weakly-Supervised Learning (ICLR2019)CODE.pytorch
- (WSGN) Weakly Supervised Gaussian Networks for Action Detection (Arxiv 2019.4)
- (RefineLoc) RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization (Arxiv 2019.4)
- (STAR) Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection (AAAI 2019)
- (TSRNet) Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision (AAAI 2019)
- (StepByStep) Step-by-step Erasion, One-by-one Collection: AWeakly Supervised Temporal Action Detector (MM 2018)
- (W-TALC) W-TALC: Weakly-supervised Temporal Activity Localization and Classification (ECCV 2018) CODE.pytorch
- (AutoLoc) AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos (ECCV 2018) CODE.caffe
- (STPN) Weakly Supervised Action Localization by Sparse Temporal Pooling Network (CVPR 2018) CODE.tensorflow.unofficial
- (H&S) Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization (ICCV 2017)
- (UNet) UntrimmedNets for Weakly Supervised Action Recognition and Detection (CVPR 2017) CODE.caffe
THUMOS14
- C3D: link
- I3D: Video is sampled at 25 frames per second. 16 frames as a video unit. link
- UNet: link
- ANet2016-cuhk(4096dims): 6 frames as a video unit. link
- ANet2016-cuhk(3072dims): 5 frames as a video unit. link
ActivityNet v1.2
ActivityNet v1.3
- C3D: link
- ANet2016-cuhk(400dims): 16 frames as a video unit. link
- I3D: 16 frames as a video unit. link
- ANet2016-cuhk(3072dims): 16 frames as a video unit. link
These methods are listed in chronological order.
| Method | Feature | IoU-> | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 |
|---|---|---|---|---|---|---|---|---|---|
| BaSNet | I3D | 58.2 | 52.3 | 44.6 | 36.0 | 27.0 | 18.6 | 10.4 | |
| 3C-Net | I3D | 59.1 | 53.5 | 44.2 | 34.1 | 26.6 | 8.1 | ||
| TSM | I3D | 39.5 | 31.9 | 24.5 | 13.8 | 7.1 | |||
| CleanNet | UNet | 37.0 | 30.9 | 23.9 | 13.9 | 7.1 | |||
| BM | I3D | 60.4 | 56.0 | 46.6 | 37.5 | 26.8 | 17.6 | 8.6 | |
| ASSG | I3D | 65.6 | 59.4 | 50.4 | 38.7 | 25.4 | 15.0 | 6.6 | |
| MAAN | I3D | 59.8 | 50.8 | 41.1 | 30.6 | 20.3 | 12.0 | 6.9 | |
| CMCS | I3D | 57.4 | 50.8 | 41.2 | 32.1 | 23.1 | 15.0 | 7.0 | |
| WSGN | I3D | 55.3 | 47.6 | 38.9 | 30.0 | 21.1 | 13.9 | 8.3 | |
| RefineLoc | UNet | 33.9 | 22.1 | 6.1 | |||||
| STAR | I3D | 68.8 | 60.0 | 48.7 | 34.7 | 23.0 | |||
| TSRNet | 2-Stream(ResNet101) | 55.9 | 46.9 | 38.3 | 28.1 | 18.6 | 11.0 | 5.59 | |
| StepByStep | TSN | 45.8 | 39.0 | 31.1 | 22.5 | 15.9 | |||
| W-TALC | UNet | 49.0 | 42.8 | 32.0 | 26.0 | 18.8 | 6.2 | ||
| W-TALC | I3D | 55.2 | 49.6 | 40.1 | 31.1 | 22.8 | 7.6 | ||
| AutoLoc | UNet | 35.8 | 29.0 | 21.2 | 13.4 | 5.8 | |||
| STPN | I3D | 52.0 | 44.7 | 35.5 | 25.8 | 16.9 | 9.9 | 4.3 | |
| H&S | C3D | 36.4 | 27.8 | 19.5 | 12.7 | 6.8 | |||
| UNet | UNet | 44.4 | 37.7 | 28.2 | 21.1 | 13.7 |