For each method, I will provide the name of baseline in brackets if it has.
Sup.:I-image-level class label, B-bounding box label, S-scribble label, P-point label.
Bac. C: Method for generating pseudo label, or backbone of the classification network.
Arc. S: backbone and method of the segmentation network.
Pre.s : The dataset used to pre-train the segmentation network, "I" denotes ImageNet, "C" denotes COCO. Note that many works use COCO pre-trained DeepLab model but not mentioned in the paper.
For methods that use multiple backbones, I only reports the results of ResNet101.
"-" indicates no fully-supervised model is utilized, "?" indicates the corresponding item is not mentioned in the paper.
Image-level supervision with extra data
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
Pre.S
val
test
SEC
ECCV16
VGG16
VGG16 DeepLabv1
I
Saliency
I
50.7
51.7
DSRG (SEC)
CVPR18
VGG16
ResNet101 DeepLabv2
I
Saliency
I
61.4
63.2
AISI
ECCV18
ResNet101
ResNet101 DeepLabv2
I
Saliency
?
63.6
64.5
Ficklenet (DSRG)
CVPR19
VGG16
ResNet101 DeepLabv2
I
Saliency
I
64.9
65.3
AISI
ECCV18
ResNet101
ResNet101 DeepLabv2
I
Saliency 24KImageNet
?
64.5
65.6
OAA
ICCV19
VGG16
ResNet101 DeepLabv1
I
Saliency
I
65.2
66.4
Zhang et al.
ECCV20
ResNet50
ResNet50 DeepLabv2
I
Saliency
?
66.6
66.7
Fan et al.
ECCV20
ResNet38
ResNet101 DeepLabv1
I
Saliency
?
67.2
66.7
MCIS
ECCV20
VGG16
ResNet101 DeepLabv1
I
Saliency
?
66.2
66.9
Lee et al.
ICCV19
VGG16
ResNet101 DeepLabv2
I
Saliency Web
I
66.5
67.4
LIID
PAMI20
ResNet50
ResNet101 DeepLabv2
I
Saliency
?
66.5
67.5
MCIS
ECCV20
VGG16
ResNet101 DeepLabv1
I
Saliency Web
?
67.7
67.5
ICD
CVPR20
VGG16
ResNet101 DeepLabv1
I
Saliency
?
67.8
68.0
LIID
PAMI20
ResNet50
ResNet101 DeepLabv2
I
Saliency 24KImageNet
?
67.8
68.3
Li et al.
AAAI21
ResNet101
ResNet101 DeepLabv2
I
Saliency
?
68.2
68.5
Yao et al.
CVPR21
VGG16
ResNet101 DeepLabv2
I
Saliency
I
68.3
68.5
AuxSegNet
ICCV21
ResNet38
-
I
Saliency
?
69.0
68.6
SPML (Ficklenet)
ICLR21
VGG16
ResNet101 DeepLabv2
I
Saliency
I
69.5
71.6
Yao et al.
CVPR21
VGG16
ResNet101 DeepLabv2
I
Saliency
I+C
70.4
70.2
WegFormer
CVPR22
Deit-B
ResNet101 DeepLabv ?
I
Saliency
I
70.5
70.3
WegFormer
CVPR22
Deit-B
ResNet101 DeepLabv ?
I
Saliency
I+C
70.9
70.5
EDAM
CVPR21
ResNet38
ResNet101 DeepLabv2
I
Saliency
?
70.9
70.6
EPS
CVPR21
ResNet38
ResNet101 DeepLabv2
I
Saliency
I
70.9
70.8
EPS
CVPR21
ResNet38
ResNet101 DeepLabv1
I
Saliency
I
71.0
71.8
DRS
AAAI21
VGG16
ResNet101 DeepLabv2
I
Saliency
I+C
71.2
71.4
L2G
CVPR22
L2G
ResNet101 DeepLabv1
I
Saliency
?
72.0
73.0
L2G
CVPR22
L2G
ResNet101 DeepLabv2
I
Saliency
?
72.1
71.7
Image-level supervision without extra data
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
Pre.S
val
test
AffinityNet
CVPR18
ResNet38
ResNet38
I
-
?
61.7
63.7
ICD
CVPR20
VGG16
ResNet101 DeepLabv1
I
-
?
64.1
64.3
IRN
CVPR19
ResNet50
ResNet50 DeepLabv2
I
-
I
63.5
64.8
IAL
IJCV20
ResNet?
ResNet?
I
-
I
64.3
65.4
SSDD (PSA)
ICCV19
ResNet38
ResNet38
I
-
I
64.9
65.5
SEAM
CVPR20
ResNet38
ResNet38 DeepLabv2
I
-
I
64.5
65.7
Chang et al.
CVPR20
ResNet38
ResNet101 DeepLabv2
I
-
?
66.1
65.9
RRM
AAAI20
ResNet38
ResNet101 DeepLabv2
I
-
?
66.3
66.5
BES
ECCV20
ResNet50
ResNet101 DeepLabv2
I
-
?
65.7
66.6
AFA
CVPR22
MiT-B1
-
I
-
?
66.0
66.3
CONTA (+SEAM)
NeurIPS20
ResNet38
ResNet101 DeepLabv2
I
-
?
66.1
66.7
Ru et al.
IJCAI21
ResNet101
ResNet101 DeepLabv2
I
-
?
67.2
67.3
WSGCN (IRN)
ICME21
ResNet50
ResNet101 DeepLabv2
I
-
I
66.7
68.8
CPN
ICCV21
ResNet38
ResNet38 DeepLabv1
I
-
?
67.8
68.5
RPNet
TMM21
ResNet101
ResNet50 DeepLabv2
I
-
I
68.0
68.2
AdvCAM
CVPR21
ResNet50
ResNet101 DeepLabv2
I
-
I
68.1
68.0
PMM
ICCV21
ResNet38
ResNet38 PSPnet
I
-
?
68.5
69.0
WSGCN (IRN)
ICME21
ResNet50
ResNet101 DeepLabv2
I
-
I+C
68.7
69.3
PMM
ICCV21
Res2Net101
Res2Net101 PSPnet
I
-
?
70.0
70.5
MCTformer
CVPR22
DeiT-S
ResNet38 DeeplabV1
I
-
?
71.9
71.6
Box-level supervision
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
Pre.S
val
test
BBAM
CVPR21
?
ResNet101 DeepLabv2
B
MCG
I
73.7
73.7
WSSL
ICCV15
-
VGG16 DeepLabv1
B
-
I
60.6
62.2
Song et al.
CVPR19
-
ResNet101 DeepLabv1
B
-
I
70.2
-
SPML (Song et al.)
ICLR21
-
ResNet101 DeepLabv2
B
-
I
73.5
74.7
Oh et al.
CVPR21
ResNet101
ResNet101 DeepLabv2
B
-
I+C
74.6
76.1
Scribble-level supervision
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
Pre.S
val
test
Scribblesup
S
NormalCut
CVPR18
-
ResNet101 DeepLabv1
S
Saliency
?
74.5
-
KernelCut
ECCV18
-
ResNet101 DeepLabv1
S
-
?
75.0
-
BPG
IJCAI19
-
ResNet101 DeepLabv2
S
-
?
76.0
-
SPML (KernelCut)
ICLR21
-
ResNet101 DeepLabv2
S
-
I
76.1
-
A2GNN
TPAMI21
-
?
S
-
?
76.2
76.1
DFR
arxiv21
-
UperNet+Swin Transformer
S
22KImageNet
-
82.8
82.9
Point-level supervision
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
Pre.S
val
test
WhatsPoint
ECCV16
-
VGG16 FCN
P
Objectness
I
46.1
-
PCAM
arxiv20
ResNet50
DeepLabv3+
P
-
?
70.5
-
1.2. Results on MS-COCO dataset
Image-level supervision with extra data
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
val
test
AuxSegNet
ICCV21
ResNet38
-
I
Saliency
33.9
-
EPS
CVPR21
ResNet38
ResNet101 DeepLabv2
I
Saliency
35.7
-
L2G
CVPR22
L2G
VGG16 DeepLabv2
I
Saliency
42.7
-
L2G
CVPR22
L2G
ResNet101 DeepLabv2
I
Saliency
44.2
-
Image-level supervision without extra data
Method
Pub.
Bac. C
Arc. S
Sup.
Extra data
val
test
MCTformer
CVPR22
DeiT-S
ResNet38 DeeplabV1
I
-
42.0
-
2. Paper List
2.1. supervised by image tags (I)
2022
MCTformer: Multi-class Token Transformer for Weakly Supervised Semantic Segmentation CVPR2022
AFA: Learning Affinity from Attention End-to-End Weakly-Supervised Semantic Segmentation with Transformers CVPR2022
WegFormer: WegFormer Transformers for Weakly Supervised Semantic Segmentation CVPR2022
L2G: L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation CVPR2022
2021
SPML: "Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning" ICLR2021
Li et al.: "Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation" AAAI2021
DRS: "Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation" AAAI2021
AdvCAM: " Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation" CVPR2021
**Yao et al. **: "Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation" CVPR2021
Chang et al.: "Weakly-Supervised Semantic Segmentation via Sub-category Exploration" CVPR2020
ICD: "Learning Integral Objects with Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation" CVPR2020
Fan et al.: "Employing multi-estimations for weakly-supervised semantic segmentation" ECCV2020
MCIS: "Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation" 2020
BES: "Weakly Supervised Semantic Segmentation with Boundary Exploration" ECCV2020
CONTA: "Causal intervention for weakly-supervised semantic segmentation" NeurIPS2020
Method: "Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentation" 2020arXiv
Zhang et al.: "Splitting vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation" ECCV2020
LIID "Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation" TPAMI2020
2019
IRN: "Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations" CVPR2019
Ficklenet: " Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference" CVPR2019
Lee et al.: "Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation" ICCV2019
OAA: "Integral Object Mining via Online Attention Accumulation" ICCV2019
SSDD: "Self-supervised difference detection for weakly-supervised semantic segmentation" ICCV2019
2018
DSRG: "Weakly-supervised semantic segmentation network with deep seeded region growing" CVPR2018
AffinityNet: "Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation" CVPR2018
GAIN: " Tell me where to look: Guided attention inference network" CVPR2018
WhatsPoint: "What’s the Point: Semantic Segmentation with Point Supervision" ECCV2016
PCAM: "PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision" arxiv2020
3. Dataset
PASCAL VOC 2012
@article{everingham2010pascal,
title={The pascal visual object classes (voc) challenge},
author={Everingham, Mark and Van Gool, Luc and Williams, Christopher KI and Winn, John and Zisserman, Andrew},
journal={International journal of computer vision},
volume={88},
number={2},
pages={303--338},
year={2010},
publisher={Springer}
}
MS COCO 2014
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={European conference on computer vision},
pages={740--755},
year={2014},
organization={Springer}
}