Papers and Results of Temporal Action Localization
Weakly Supervised Performance on THUMOS'14 dataset.
The detectors are sorted by the mAP with threshold 0.5.
"c" indicates whether release code, yes (Y) or no (N).
"e" indicates the evaluation code, THUMOS (T), ActivityNet (A) or implemented by themselves.
Detector
Pub
c
e
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
avg
info
D2-Net
arXiv-20-12-11
N
A
65.6
60.0
52.1
43.3
35.9
-
-
-
-
-
The same author with 3C-Net
Lee et al
AAAI21
Y
A
67.5
61.2
52.3
43.4
33.7
22.9
12.1
-
-
-
The same author with BaS-Net
HAM-Net
AAAI21
N
A
65.9
59.6
52.2
43.1
32.6
21.9
12.5
-
-
-
-
ACSNet
AAAI21
N
A
-
-
51.4
42.7
32.4
22.0
11.7
-
-
-
-
EM-MIL
ECCV20
N
A
59.1
52.7
45.5
36.8
30.5
22.7
16.4
-
-
-
Use existing classifiation results
A2CL-PT
ECCV20
Y
A
61.2
56.1
48.1
39.0
30.1
19.2
10.6
4.8
1.0
30.0
Report unsupervised performance as well
ACL
CVPR20
N
A
-
-
46.9
38.9
30.1
19.8
10.4
-
-
-
Report unsupervised performance as well
Liu et al.
AAAI21
N
A
61.7
58.0
50.8
41.7
29.6
20.1
10.7
4.3
0.5
-
-
WSTAL
WACV20
-
62.3
-
46.8
-
29.6
-
9.7
-
-
-
-
ActionBytes
CVPR20
N
A
-
-
43.0
37.5
29.0
-
9.5
-
-
-
-
DGAM
CVPR20
Y
A
60.0
54.2
46.8
38.2
28.8
19.8
11.4
3.6
0.4
-
-
TSCN
ECCV20
N
A
63.4
57.6
47.8
37.7
28.7
19.4
10.2
3.9
0.7
-
-
BaSNet-I3D
AAAI20
Y
A
58.2
52.3
44.6
36.0
27.0
18.6
10.4
3.9
0.5
-
-
BaSNet-UNT
AAAI20
Y
A
56.2
50.3
42.8
34.7
25.1
17.1
9.3
3.7
0.5
-
-
WSBM
ICCV19
N
A
60.4
56.0
46.6
37.5
26.8
17.6
9.0
3.3
0.4
-
-
3C-Net
ICCV19
Y
I
59.1
53.5
44.2
34.1
26.6
-
8.1
-
-
-
-
ASSG
ACM 19
N
65.6
59.4
50.4
38.7
25.4
15.0
6.6
-
-
-
-
TSM
ICCV19
N
T
-
-
39.5
31.9
24.5
13.8
7.1
-
-
23.4
-
CleanNet
ICCV19
N
T
-
-
37.0
30.9
23.9
13.9
7.1
-
-
-
-
CMCS-I3D
CVPR19
Y
T
57.4
50.8
41.2
32.1
23.1
15.0
7.0
-
-
-
report avg-mAP
CMCS-UNT
CVPR19
Y
T
53.5
46.8
37.5
29.1
19.9
12.3
6.0
-
-
-
-
STARNet
AAAI19
N
A
68.8
60.0
48.7
34.7
23.0
-
-
-
-
-
-
W-TALC
ECCV18
Y
I
55.2
49.6
40.1
31.1
22.8
-
-
-
-
7.6
-
AutoLoc
ECCV18
Y
T
-
-
35.8
29.0
21.2
13.4
5.8
-
-
-
-
MAAN
ICLR19
Y
A
59.8
50.8
41.1
30.6
20.3
12.0
6.9
2.6
0.2
24.9
-
LTSR
AAAI19
N
T
55.9
46.9
38.3
28.1
18.6
11.0
5.59
2.19
0.29
-
-
WSGN
WACV20
-
T
51.1
44.4
34.9
26.3
18.1
11.6
6.5
-
-
-
-
STPN
CVPR18
I
A
52.0
44.7
35.5
25.8
16.9
9.9
4.3
1.2
0.1
-
-
CPMN
ACCV18
N
T
47.1
41.6
32.8
24.7
16.1
10.1
5.5
-
-
-
-
S-O-C
ACM18
N
T
45.8
39.0
31.1
22.5
15.9
-
-
-
-
-
-
UntrimmedNets
CVPR17
Y
T
44.4
37.7
28.2
21.1
13.7
-
-
-
-
-
-
H&S
ICCV17
Y
T
36.44
27.84
19.49
12.66
6.84
-
-
-
-
-
-
LPAT-I3D+TEM
arXiv
-
-
-
46.9
37.4
28.0
16.6
9.2
-
-
27.6
-
LPAT-I3D
arXiv
-
-
-
46.7
37.5
27.9
17.6
9.2
-
-
27.6
-
LPAT-U
arXiv
-
-
-
39.9
31.5
22.6
14.2
7.9
-
-
27.6
-
RefineLoc-I3D
arXiv
-
T
-
-
40.8
-
23.1
-
5.3
-
-
-
-
RefineLoc-TSN
arXiv
-
T
-
-
36.1
-
22.6
-
5.8
-
-
-
-
Weakly Supervised Performance on ActivityNet v1.2 dataset.
Detector
Pub
c
0.5
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
avg
test
info
D2-Net
arXiv-20-12-11
N
42.3
-
-
-
-
25.5
-
-
-
5.8
26.0
-
-
ACSNet
AAAI21
N
40.1
-
-
-
-
26.1
-
-
-
6.8
26.0
-
-
Lee et al
AAAI21
N
41.2
-
-
-
-
25.6
-
-
-
6.0
25.9
-
-
Liu at al.
AAAI21
N
39.2
-
-
-
-
25.6
-
-
-
6.8
25.5
-
-
HAM-Net
AAAI21
N
41.0
-
-
-
-
24.8
-
-
-
5.3
25.1
-
-
BaSNet
AAAI20
Y
38.5
-
-
-
-
24.2
-
-
-
5.6
24.3
-
-
TSCN
ECCV20
N
37.6
-
-
-
-
23.7
-
-
-
5.7
23.6
-
-
CMCS
CVPR19
Y
36.8
-
-
-
-
22.0
-
-
-
5.6
22.4
-
-
3C-Net
ICCV19
Y
35.4
-
-
-
-
22.9
-
-
-
8.5
21.1
-
-
TSM
ICCV19
N
28.3
26.0
23.6
21.2
18.9
17.0
14.0
11.1
7.5
3.5
-
-
-
CleanNet
ICCV19
N
37.1
33.4
29.9
26.7
23.4
20.3
17.2
13.9
9.2
5.0
21.6
-
-
EM-MIL
ECCV20
N
37.4
-
-
-
23.1
-
-
-
2.0
-
20.3
-
-
W-TALC
ECCV18
Y
37.0
-
-
-
14.6
-
-
-
-
-
18.0
-
-
AutoLoc
ECCV18
Y
27.3
24.9
22.5
19.9
17.5
15.1
13.0
10.0
6.8
3.3
16.0
-
-
RefineLoc-I3D
arXiv
-
38.7
-
-
-
-
22.6
-
-
-
5.5
23.2
-
-
RefineLoc-TSN
arXiv
-
38.8
-
-
-
-
22.2
-
-
-
5.3
23.2
-
-
LPAT
arXiv
-
37.6
34.6
31.6
28.7
25.6
22.6
19.6
15.3
10.9
4.9
23.1
-
-
WSTAL
arXiv
-
35.2
-
-
-
16.3
-
-
-
-
-
-
-
-
Weakly Supervised Performance on ActivityNet v1.3 dataset.
Detector
Pub
c
0.5
0.75
0.95
avg
ACSNet
AAAI21
N
36.3
24.2
5.8
23.9
Lee et al.
AAAI21
Y
37.0
23.9
5.7
23.7
Liu et al.
AAAI21
N
35.1
23.7
5.6
23.2
A2CL-PT
ECCV20
Y
36.8
22.0
5.2
22.5
BaSNet-I3D
AAAI20
Y
34.5
22.5
4.9
22.2
TSCN
ECCV20
N
35.3
21.4
5.3
21.7
WSBM
ICCV19
N
36.4
19.2
2.9
-
ASSG
ACM 19
N
32.3
20.1
4.0
-
TSM
ICCV19
N
30.0
19.0
4.5
-
CMCS
CVPR19
Y
34.0
20.9
5.7
21.2
STARNet
AAAI19
N
31.1
18.8
4.7
-
MAAN
ICLR19
Y
33.7
21.9
5.5
-
LTSR
AAAI19
N
33.1
18.7
3.32
21.78
STPN
CVPR18
I
29.3
16.9
2.6
-
CPMN
ACCV18
N
39.29
24.09
6.71
24.42
S-O-C
ACM18
N
27.3
14.7
2.9
15.6
Weakly Supervised Temporal Action Localization
D2-Net: Sanath Narayan, Hisham Cholakkal, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao.
"D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations" arXiv:2012.06440.
[paper]
Lee et al: Pilhyeon Lee, Jinglu Wang, Yan Lu, Hyeran Byun.
"Weakly-supervised Temporal Action Localization by Uncertainty Modeling" AAAI 2021.
[paper]
[code]
HAM-Net: Ashraful Islam, Chengjiang Long , Richard J. Radke.
"A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization" AAAI 2021.
[paper]
[code]
Liu et al.: Ziyi Liu, Le Wang, Qilin Zhang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua.
"ACSNet : Action-Context Separation Network for Weakly Supervised Temporal Action Localization" AAAI 2021.
[paper]
Liu at al.: Ziyi Liu, Le Wang, Wei Tang, Junsong Yuan, Nanning Zheng, Gang Hua.
"Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context" AAAI 2021.
[paper]
TSCN: Zhai, Yuanhao and Wang, Le and Tang, Wei and Zhang, Qilin and Yuan, Junsong and Hua, Gang.
"Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization." ECCV 2020.
[paper]
3C-Net: Sanath Narayan, Hisham Cholakkal, Fahad Shabaz Khan, Ling Shao.
"3C-Net : Category Count and Center Loss for Weakly-Supervised Action Localization." ICCV 2019.
[paper]
[code]
ASSG: Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Yi Niu, Shiliang, Pu Fei Wu, Futai Zou.
"Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization" ACM MM 2019.
[paper]
**AutoLoc:**Zheng Shou, Hang Gao, Lei Zhang, KazuyukiMiyazawa, Shih-Fu Chang.
"AutoLoc Weakly-supervised Temporal Action Localization in Untrimmed Videos"ECCV 2018.
[paper]
[code]
**CPMN:**Haisheng Su, Xu Zhao, Tianwei Lin.
"Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization"ACCV 2018.
[paper]
**H&S:**Krishna Kumar Singh, Yong Jae Lee.
"Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization"ICCV 2017.
[paper]
[code]
**LTSR:**Xiao-Yu Zhang, Haichao Shi, Changsheng Li, Kai Zheng, Xiaobin Zhu, Lixin Duan.
"Learning Transferable Self-Attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision"AAAI 2019.
[paper]
**WSGN:**Basura Fernando, Cheston Tan Yin Chet.
"Weakly Supervised Gaussian Networks for Action Detection" WACV(2020)
**MAAN:**Yuan Yuan, Yueming Lyu, Xi Shen, Ivor W. Tsang, Dit-Yan Yeung.
"MARGINALIZED AVERAGE ATTENTIONAL NETWORK FOR WEAKLY-SUPERVISED LEARNING"ICLR 2019.
[paper]
[code]
**S-O-C:**Jia-Xing Zhong, Nannan Li, Weijie Kong, Tao Zhang, Thomas H. Li, Ge Li.
" Step-by-step Erasion, One-by-one Collection: A Weakly Supervised Temporal Action Detector"ACM MM 2018.
[paper]
W-TALCSujoy Paul, Sourya Roy, Amit K Roy-Chowdhury.
"W-TALC: Weakly-supervised Temporal Activity Localization and Classification" ECCV 2018.
[paper]
[code]
LPATXudong, Lin Zheng, Shou Shih-Fu Chang.
"LPAT: Learning to Predict Adaptive Threshold for Weakly-supervised Temporal Action Localization" arXiv 2019.
[paper]
**RefineLoc:**Humam Alwassel1, Alejandro Pardo1, Fabian Caba Heilbron, Ali Thabet1 Bernard Ghanem1.
"RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization"Arxiv(2019)
[paper]
[paper]
Expecting for paper
lvr: Xingyu Liu, Joon-Young Lee, Hailin Jin.
"Learning Video Representations from Correspondence Proposals." CVPR 2019 oral.
Dataset
THUMOS'14: Yu-Gang Jiang, Jingen Liu, Amir R. Zamir, George Toderici.
"THUMOS Challenge 2014"
[project]
Activity: Bernard Ghanem, Juan Carlos Niebles, Cees Snoek, Fabian Caba Heilbron, Humam Alwassel, Victor Escorcia.
"A Large-Scale Video Benchmark for Human Activity Understanding"
[project]
THUMOS'15: Alexander Gorban, Haroon Idrees, Yu-Gang Jiang, Amir R. Zamir.
"THUMOS Challenge 2015"
[project]
COIN: Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, Jie Zhou.
"COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis." CVPR 2019.
[paper]
[project]
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