All Projects → Pilhyeon → Awesome-Weakly-Supervised-Temporal-Action-Localization

Pilhyeon / Awesome-Weakly-Supervised-Temporal-Action-Localization

Licence: other
A curated publication list on weakly-supervised temporal action localization

Projects that are alternatives of or similar to Awesome-Weakly-Supervised-Temporal-Action-Localization

Mmaction
An open-source toolbox for action understanding based on PyTorch
Stars: ✭ 1,711 (+2532.31%)
Mutual labels:  temporal-action-detection, temporal-action-localization
TadTR
End-to-end Temporal Action Detection with Transformer. [Under review for a journal publication]
Stars: ✭ 55 (-15.38%)
Mutual labels:  temporal-action-detection, temporal-action-localization
Learning-Action-Completeness-from-Points
Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)
Stars: ✭ 53 (-18.46%)
Mutual labels:  weakly-supervised-learning, temporal-action-localization
MUSES
[CVPR 2021] Multi-shot Temporal Event Localization: a Benchmark
Stars: ✭ 51 (-21.54%)
Mutual labels:  temporal-action-detection, temporal-action-localization
TS-CAM
Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.
Stars: ✭ 96 (+47.69%)
Mutual labels:  weakly-supervised-learning
trove
Weakly supervised medical named entity classification
Stars: ✭ 55 (-15.38%)
Mutual labels:  weakly-supervised-learning
WeSHClass
[AAAI 2019] Weakly-Supervised Hierarchical Text Classification
Stars: ✭ 83 (+27.69%)
Mutual labels:  weakly-supervised-learning
Materials-Temporal-Action-Detection
temporal action detection: benchmark results, features download etc.
Stars: ✭ 199 (+206.15%)
Mutual labels:  temporal-action-detection
HiGitClass
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories (ICDM'19)
Stars: ✭ 58 (-10.77%)
Mutual labels:  weakly-supervised-learning
just-ask
[TPAMI Special Issue on ICCV 2021 Best Papers, Oral] Just Ask: Learning to Answer Questions from Millions of Narrated Videos
Stars: ✭ 57 (-12.31%)
Mutual labels:  weakly-supervised-learning
Awesome-Weak-Shot-Learning
A curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.
Stars: ✭ 142 (+118.46%)
Mutual labels:  weakly-supervised-learning
C2C
Implementation of Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification approach.
Stars: ✭ 30 (-53.85%)
Mutual labels:  weakly-supervised-learning
dcsp segmentation
No description or website provided.
Stars: ✭ 34 (-47.69%)
Mutual labels:  weakly-supervised-learning
weasel
Weakly Supervised End-to-End Learning (NeurIPS 2021)
Stars: ✭ 117 (+80%)
Mutual labels:  weakly-supervised-learning
GAL-fWSD
Generative Adversarial Learning Towards Fast Weakly Supervised Detection
Stars: ✭ 18 (-72.31%)
Mutual labels:  weakly-supervised-learning
deviation-network
Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection
Stars: ✭ 94 (+44.62%)
Mutual labels:  weakly-supervised-learning
WSDEC
Weakly Supervised Dense Event Captioning in Videos, i.e. generating multiple sentence descriptions for a video in a weakly-supervised manner.
Stars: ✭ 95 (+46.15%)
Mutual labels:  weakly-supervised-learning
SPML
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
Stars: ✭ 81 (+24.62%)
Mutual labels:  weakly-supervised-learning
Simple-does-it-weakly-supervised-instance-and-semantic-segmentation
Weakly Supervised Segmentation by Tensorflow. Implements semantic segmentation in Simple Does It: Weakly Supervised Instance and Semantic Segmentation, by Khoreva et al. (CVPR 2017).
Stars: ✭ 46 (-29.23%)
Mutual labels:  weakly-supervised-learning
WeFEND-AAAI20
Dataset for paper "Weak Supervision for Fake News Detection via Reinforcement Learning" published in AAAI'2020.
Stars: ✭ 67 (+3.08%)
Mutual labels:  weakly-supervised-learning

Awesome Weakly-supervised Temporal Action Localization

A curated publication list on weakly-supervised temporal action localization.

This repository was built to facilitate navigating the mainstream on weakly-supervised temporal action localization.
Please note that only accepted papers (for reliability) by conferences (for brevity) are contained here.

Last updated: 2021/12/31 (AAAI'22 added)

Table of Contents

Performance Tables

The mean average precisions (mAPs) under the standard intersection over union (IoU) thresholds are reported. For example, '@0.5' indicates the mAP score at the IoU threshold of 0.5.
The AVG denotes the average mAP under the IoU thresholds from 0.1 to 0.7 (for THUMOS14) or from 0.5 to 0.95 with a step size of 0.05 (for ActivityNet both versions).

In addition, links to the implementations are attached with their framework specification if available. 'o-' and 'u-' indicate the official and the unofficial implementations, respectively.

[Note]
*: use of additional trimmed videos
†: use of additional information such as action count, pose, and audio

THUMOS14

ID Year Venue Model
(or Authors)
@0.1 @0.2 @0.3 @0.4 @0.5 @0.6 @0.7 AVG code
1 2017 CVPR UntrimmedNets 44.4 37.7 28.2 21.1 13.7 - - - [o-matlab]
2 2017 ICCV Hide-and-seek 36.4 27.8 19.5 12.7 6.8 - - - [o-torch]
3 2018 CVPR STPN 52.0 44.7 35.5 25.8 16.9 9.9 4.3 27.0 [u-tensorflow]
4 2018 ECCV AutoLoc - - 35.8 29.0 21.2 13.4 5.8 - [o-caffe]
5 2018 ECCV W-TALC 55.2 49.6 40.1 31.1 22.8 - 7.6 - [o-pytorch]
[o-tensorflow]
6 2018 MM Zhong et al. 45.8 39.0 31.1 22.5 15.9 - - -
7 2019 AAAI TSRNet* 55.9 46.9 38.3 28.1 18.6 11.0 5.6 29.2
8 2019 AAAI STAR† 68.8 60.0 48.7 34.7 23.0 - - -
9 2019 ICLR MAAN 59.8 50.8 41.1 30.6 20.3 12.0 6.9 31.6 [o-pytorch]
10 2019 CVPR Liu et al. 57.4 50.8 41.2 32.1 23.1 15.0 7.0 32.4 [o-pytorch]
11 2019 ICIP Park et al. - - 40.2 32.2 21.7 - 9.2 -
12 2019 ICIP ACN - - 35.9 30.7 24.2 15.7 7.4 -
13 2019 MM ASSG 65.6 59.4 50.4 38.7 25.4 15.0 6.6 37.3
14 2019 ICCV CleanNet - - 37.0 30.9 23.9 13.9 7.1 -
15 2019 ICCV TSM - - 39.5 - 24.5 - 7.1 -
16 2019 ICCV 3C-Net† 59.1 53.5 44.2 34.1 26.6 - 8.1 - [o-pytorch]
17 2019 ICCV Nguyen et al. 60.4 56.0 46.6 37.5 26.8 17.6 9.0 36.3
18 2020 AAAI PreTrimNet† 57.5 50.7 41.4 32.1 23.1 14.2 7.7 32.4
19 2020 AAAI BaS-Net 58.2 52.3 44.6 36.0 27.0 18.6 10.4 35.3 [o-pytorch]
20 2020 AAAI RPN 62.3 57.0 48.2 37.2 27.9 16.7 8.1 36.8
21 2020 WACV WSGN 57.9 51.2 42.0 33.1 25.1 16.7 8.9 33.6
22 2020 WACV Islam and Radke 62.3 - 46.8 - 29.6 - 9.7 - [o-pytorch]
23 2020 WACV Rashid et al. 63.7 56.9 47.3 36.4 26.1 - - - [o-pytorch]
24 2020 CVPR ActionBytes - - 43.0 35.8 29.0 - 9.5 -
25 2020 CVPR DGAM 60.0 54.2 46.8 38.2 28.8 19.8 11.4 37.0 [o-pytorch]
26 2020 CVPR Gong et al. - - 46.9 38.9 30.1 19.8 10.4 - [o-pytorch]
27 2020 ECCV EM-MIL 59.1 52.7 45.5 36.8 30.5 22.7 16.4 37.7
28 2020 ECCV A2CL-PT 61.2 56.1 48.1 39.0 30.1 19.2 10.6 37.8 [o-pytorch]
29 2020 ECCV TSCN 63.4 57.6 47.8 37.7 28.7 19.4 10.2 37.8
30 2020 MM ACM-BANet 64.6 57.7 48.9 40.9 32.3 21.9 13.5 40.0
31 2021 WACV RefineLoc - - 40.8 32.7 23.1 13.3 5.3 - [o-pytorch]
32 2021 AAAI Liu et al. - - 50.8 41.7 29.6 20.1 10.7 -
33 2021 AAAI ACSNet - - 51.4 42.7 32.4 22.0 11.7 -
34 2021 AAAI HAM-Net 65.9 59.6 52.2 43.1 32.6 21.9 12.5 41.1 [o-pytorch]
35 2021 AAAI Lee et al. 67.5 61.2 52.3 43.4 33.7 22.9 12.1 41.9 [o-pytorch]
37 2021 CVPR ASL 67.0 - 51.8 - 31.1 - 11.4 - [o-pytorch]
38 2021 CVPR CoLA 66.2 59.5 51.5 41.9 32.2 22.0 13.1 40.9 [o-pytorch]
39 2021 CVPR AUMN 66.2 61.9 54.9 44.4 33.3 20.5 9.0 41.5
40 2021 CVPR TS-PCA 67.6 61.1 53.4 43.4 34.3 24.7 13.7 42.6
41 2021 CVPR UGCT 69.2 62.9 55.5 46.5 35.9 23.8 11.4 43.6
42 2021 ICCV D2-Net 65.7 60.2 52.3 43.4 36.0 - - - [o-pytorch]
43 2021 ICCV FAC-Net 67.6 62.1 52.6 44.3 33.4 22.5 12.7 42.2 [o-pytorch]
44 2021 MM CSCL 68.0 61.8 52.7 43.3 33.4 21.8 12.3 41.9
45 2021 MM CO2-Net 70.1 63.6 54.5 45.7 38.3 26.4 13.4 44.6
46 2022 AAAI ACGNet 68.1 62.6 53.1 44.6 34.7 22.6 12.0 42.5

ActivityNet1.2

ID Year Venue Model
(or Authors)
@0.5 @0.75 @0.95 AVG code
4 2018 ECCV AutoLoc 27.3 15.1 3.3 16.0 [o-caffe]
5 2018 ECCV W-TALC 37.0 - - 18.0 [o-pytorch]
[o-tensorflow]
6 2018 MM Zhong et al. 27.3 14.7 2.9 15.6
10 2019 CVPR Liu et al. 36.8 22.0 5.6 22.4 [o-pytorch]
11 2019 ICIP Park et al. 33.7 - - -
12 2019 ICIP ACN 30.4 15.4 3.7 17.0
14 2019 ICCV CleanNet 37.1 20.3 5.0 21.6
15 2019 ICCV TSM 28.3 17.0 3.5 17.1
16 2019 ICCV 3C-Net† 37.2 - - 21.7 [o-pytorch]
19 2020 AAAI BaS-Net 38.5 24.2 5.6 24.3 [o-pytorch]
20 2020 AAAI RPN 37.6 23.9 5.4 23.3
22 2020 WACV Islam and Radke 35.2 - - - [o-pytorch]
23 2020 WACV Rashid et al. 29.4 - - - [o-pytorch]
24 2020 CVPR ActionBytes 39.4 - - -
25 2020 CVPR DGAM 41.0 23.5 5.3 24.4 [o-pytorch]
26 2020 CVPR Gong et al. 40.0 25.0 4.6 24.6 [o-pytorch]
27 2020 ECCV EM-MIL 37.4 - - 20.3
29 2020 ECCV TSCN 37.6 23.7 5.7 23.6
31 2021 WACV RefineLoc 38.7 22.6 5.5 23.2 [o-pytorch]
32 2021 AAAI Liu et al. 39.2 25.6 6.8 25.5
33 2021 AAAI ACSNet 40.1 26.1 6.8 26.0
34 2021 AAAI HAM-Net 41.0 24.8 5.3 25.1 [o-pytorch]
35 2021 AAAI Lee et al. 41.2 25.6 6.0 25.9 [o-pytorch]
36 2021 ICLR Lee et al.† 44.8 26.7 1.0 26.0
37 2021 CVPR ASL 40.2 - - 25.8 [o-pytorch]
38 2021 CVPR CoLA 42.7 25.7 5.8 26.1 [o-pytorch]
39 2021 CVPR AUMN 42.0 25.0 5.6 25.5
41 2021 CVPR UGCT 41.8 25.3 5.9 25.8
42 2021 ICCV D2-Net 42.3 25.5 5.8 26.0 [o-pytorch]
44 2021 MM CSCL 43.8 26.9 5.6 26.9
45 2021 MM CO2-Net 43.3 26.3 5.2 26.4
46 2022 AAAI ACGNet 41.8 26.0 5.9 26.1

ActivityNet1.3

ID Year Venue Model
(or Authors)
@0.5 @0.75 @0.95 AVG code
3 2018 CVPR STPN 29.3 16.9 2.6 - [u-tensorflow]
7 2019 AAAI TSRNet* 33.1 18.7 3.3 21.8
8 2019 AAAI STAR† 31.1 18.8 4.7 -
9 2019 ICLR MAAN 33.7 21.9 5.5 - [o-pytorch]
10 2019 CVPR Liu et al. 34.0 20.9 5.7 21.2 [o-pytorch]
13 2019 MM ASSG 32.3 20.1 4.0 -
15 2019 ICCV TSM 30.3 19.0 4.5 -
17 2019 ICCV Nguyen et al. 36.4 19.2 2.9 -
18 2020 AAAI PreTrimNet† 34.8 20.9 5.3 22.5
19 2020 AAAI BaS-Net 34.5 22.5 4.9 22.2 [o-pytorch]
28 2020 ECCV A2CL-PT 36.8 22.0 5.2 22.5 [o-pytorch]
29 2020 ECCV TSCN 35.3 21.4 5.3 21.7
30 2020 MM ACM-BANet 37.6 24.7 6.5 24.4
32 2021 AAAI Liu et al. 35.1 23.7 5.6 23.2
33 2021 AAAI ACSNet 36.3 24.2 5.8 23.9
35 2021 AAAI Lee et al. 37.0 23.9 5.7 23.7 [o-pytorch]
39 2021 CVPR AUMN 38.3 23.5 5.2 23.5
40 2021 CVPR TS-PCA 37.4 23.5 5.9 23.7
41 2021 CVPR UGCT 39.1 22.4 5.8 23.8
43 2021 ICCV FAC-Net 37.6 24.2 6.0 24.0 [o-pytorch]

Paper List

  1. [UntrimmedNets] | CVPR'17 | UntrimmedNets for Weakly Supervised Action Recognition and Detection | [pdf] | [o-matlab]
  2. [Hide-and-seek] | ICCV'17 | Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization | [pdf] | [o-torch]
  3. [STPN] | CVPR'18 | Weakly Supervised Action Localization by Sparse Temporal Pooling Network | [pdf] | [u-tensorflow]
  4. [AutoLoc] | ECCV'18 | AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos | [pdf] | [o-caffe]
  5. [W-TALC] | ECCV'18 | W-TALC: Weakly-supervised Temporal Activity Localization and Classification | [pdf] | [o-pytorch] | [o-tensorflow]
  6. [Zhong et al.] | MM'18 | Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector | [pdf]
  7. [TSR-Net*] | AAAI'19 | Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision | [pdf]
  8. [STAR†] | AAAI'19 | Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection | [pdf]
  9. [MAAN] | ICLR'19 | Marginalized Average Attentional Network for Weakly-Supervised Learning | [pdf]
  10. [Liu et al.] | CVPR'19 | Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization | [pdf] | [o-pytorch]
  11. [Park et al.] | ICIP'19 | Graph Regularization Network with Semantic Affinity for Weakly-Supervised Temporal Action Localization | [pdf]
  12. [ACN] | ICIP'19 | Action Coherence Network for Weakly Supervised Temporal Action Localization | [pdf]
  13. [ASSG] | MM'19 | Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization | [pdf]
  14. [CleanNet] | ICCV'19 | Weakly Supervised Temporal Action Localization through Contrast based Evaluation Networks | [pdf]
  15. [TSM] | ICCV'19 | Temporal Structure Mining for Weakly Supervised Action Detection | [pdf]
  16. [3C-Net†] | ICCV'19 | 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization | [pdf] | [o-pytorch]
  17. [Nguyen et al.] | ICCV'19 | Weakly-supervised Action Localization with Background Modeling | [pdf]
  18. [PreTrimNet†] | AAAI'20 | Multi-Instance Multi-Label Action Recognition and Localization Based on Spatio-Temporal Pre-Trimming for Untrimmed Videos | [pdf]
  19. [BaS-Net] | AAAI'20 | Background Suppression Network for Weakly-supervised Temporal Action Localization | [pdf] | [o-pytorch]
  20. [RPN] | AAAI'20 | Relational Prototypical Network for Weakly Supervised Temporal Action Localization | [pdf]
  21. [WSGN] | WACV'20 | Weakly Supervised Gaussian Networks for Action Detection | [pdf]
  22. [Islam and Radke] | WACV'20 | Weakly Supervised Temporal Action Localization Using Deep Metric Learning | [pdf] | [o-pytorch]
  23. [Rashid et al.] | WACV'20 | Action Graphs: Weakly-supervised Action Localization with Graph Convolution Networks | [pdf] | [o-pytorch]
  24. [ActionBytes] | CVPR'20 | ActionBytes: Learning from Trimmed Videos to Localize Actions | [pdf]
  25. [DGAM] | CVPR'20 | Weakly-Supervised Action Localization by Generative Attention Modeling | [pdf] | [o-pytorch]
  26. [Gong et al.] | CVPR'20 | Learning Temporal Co-Attention Models for Unsupervised Video Action Localization | [pdf] | [o-pytorch]
  27. [EM-MIL] | ECCV'20 | Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning | [pdf]
  28. [A2CL-PT] | ECCV'20 | Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization | [pdf] | [o-pytorch]
  29. [TSCN] | ECCV'20 | Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization | [pdf]
  30. [ACM-BANet] | MM'20 | Action Completeness Modeling with Background Aware Networks for Weakly-Supervised Temporal Action Localization | [pdf]
  31. [RefineLoc] | WACV'21 | RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization | [pdf] | [o-pytorch]
  32. [Liu et al.] | AAAI'21 | Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context | [pdf]
  33. [ACSNet] | AAAI'21 | ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization | [pdf]
  34. [HAM-Net] | AAAI'21 | A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization | [pdf] | [o-pytorch]
  35. [Lee et al.] | AAAI'21 | Weakly-supervised Temporal Action Localization by Uncertainty Modeling | [pdf] | [o-pytorch]
  36. [Lee et al.†] | ICLR'21 | Cross-attentional Audio-visual Fusion for Weakly-supervised Action Localization | [pdf]
  37. [ASL] | CVPR'21 | Weakly Supervised Action Selection Learning in Video | [pdf] | [o-pytorch]
  38. [CoLA] | CVPR'21 | CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning | [pdf] | [o-pytorch]
  39. [AUMN] | CVPR'21 | Action Unit Memory Network for Weakly Supervised Temporal Action Localization | [pdf]
  40. [TS-PCA] | CVPR'21 | The Blessings of Unlabeled Background in Untrimmed Videos | [pdf]
  41. [UGCT] | CVPR'21 | Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection | [pdf]
  42. [D2-Net] | ICCV'21 | D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations | [pdf] | [o-pytorch]
  43. [FAC-Net] | ICCV'21 | Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization | [pdf] | [o-pytorch]
  44. [CSCL] | MM'21 | Weakly-Supervised Temporal Action Localization via Cross-Stream Collaborative Learning | [pdf]
  45. [CO2-Net] | MM'21 | Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization | [pdf]
  46. [ACGNet] | AAAI'22 | ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization | [pdf]

Feedback

If you have any suggestions or find missing papers, please feel free to contact me.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].