Awesome Weakly-supervised Semantic Segmentation （Image）

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Contact me if any paper is missed!

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1. Performance

1.1. Results on PASCAL VOC 2012 dataset

• 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.

Method	Pub.	Bac. C	Arc. S	Sup.	Extra data	Pre.S	val	test
BBAM	CVPR2021	?	ResNet101 DeepLabv2	B	MCG	I	73.7	73.7
Oh et al.	CVPR2021	ResNet101	ResNet101 DeepLabv2	B	-	I+C	74.6	76.1
WSSL	ICCV2015	-	VGG16 DeepLabv1	B	-	I	60.6	62.2
Song et al.	CVPR2019	-	ResNet101 DeepLabv1	B	-	I	70.2	-
SPML (Song et al.)	ICLR2021	-	ResNet101 DeepLabv2	B	-	I	73.5	74.7
NormalCut	CVPR2018	-	ResNet101 DeepLabv1	S	Saliancy	?	74.5	-
KernelCut	ECCV2018	-	ResNet101 DeepLabv1	S	-	?	75.0	-
BPG	IJCAI2019	-	ResNet101 DeepLabv2	S	-	?	76.0	-
SPML (KernelCut)	ICLR2021	-	ResNet101 DeepLabv2	S	-	I	76.1	-
WhatsPoint	ECCV2016	-	VGG16 FCN	P	Objectness	I	46.1	-
PCAM	arxiv2020	ResNet50	? DeepLabv3+	P	-	?	70.5	-
SEC	ECCV2016	VGG16	VGG16 DeepLabv1	I	Saliancy	I	50.7	51.7
DSRG (SEC)	CVPR2018	VGG16	ResNet101 DeepLabv2	I	Saliancy	I	61.4	63.2
Fan et al.	ECCV2018	ResNet101	ResNet101 DeepLabv2	I	Saliancy	?	63.6	64.5
Ficklenet (DSRG)	CVPR2019	VGG16	ResNet101 DeepLabv2	I	Saliancy	I	64.9	65.3
Fan et al.	ECCV2018	ResNet101	ResNet101 DeepLabv2	I	Saliancy 24KImageNet	?	64.5	65.6
OAA	ICCV2019	VGG16	ResNet101 DeepLabv1	I	Saliancy	I	65.2	66.4
Fan et al.	ECCV2020	ResNet38	ResNet101 DeepLabv1	I	Saliancy	?	67.2	66.7
MCIS	ECCV2020	VGG16	ResNet101 DeepLabv1	I	Saliancy	?	66.2	66.9
Lee et al.	ICCV2019	VGG16	ResNet101 DeepLabv2	I	Saliancy Web	I	66.5	67.4
LIID	PAMI2020	ResNet50	ResNet101 DeepLabv2	I	Saliancy	?	66.5	67.5
MCIS	ECCV2020	VGG16	ResNet101 DeepLabv1	I	Saliancy Web	?	67.7	67.5
ICD	CVPR2020	VGG16	ResNet101 DeepLabv1	I	Saliancy	?	67.8	68.0
LIID	PAMI2020	ResNet50	ResNet101 DeepLabv2	I	Saliancy 24KImageNet	?	67.8	68.3
Li et al.	AAAI2021	ResNet101	ResNet101 DeepLabv2	I	Saliancy	?	68.2	68.5
Yao et al.	CVPR2021	VGG16	ResNet101 DeepLabv2	I	Saliancy	I	68.3	68.5
Yao et al.	CVPR2021	VGG16	ResNet101 DeepLabv2	I	Saliancy	I+C	70.4	70.2
DRS	AAAI2021	VGG16	ResNet101 DeepLabv2	I	Saliancy	?	71.2	71.4
SPML (Ficklenet)	ICLR2021	VGG16	ResNet101 DeepLabv2	I	Saliancy	I	69.5	71.6
AffinityNet	CVPR2018	ResNet38	ResNet38	I	-	?	61.7	63.7
ICD	CVPR2020	VGG16	ResNet101 DeepLabv1	I	-	?	64.1	64.3
IRN	CVPR2019	ResNet50	ResNet50 DeepLabv2	I	-	I	63.5	64.8
IAL	IJCV20	ResNet?	ResNet?	I	-	I	64.3	65.4
SSDD (PSA)	ICCV2019	ResNet38	ResNet38	I	-	I	64.9	65.5
SEAM	CVPR2020	ResNet38	ResNet38 DeepLabv2	I	-	I	64.5	65.7
Chang et al.	CVPR2020	ResNet38	ResNet101 DeepLabv2	I	-	?	66.1	65.9
RRM	AAAI2020	ResNet38	ResNet101 DeepLabv2	I	-	?	66.3	66.5
BES	ECCV2020	ResNet50	ResNet101 DeepLabv2	I	-	?	65.7	66.6
CONTA (+SEAM)	NeurIPS2020	ResNet38	ResNet101 DeepLabv2	I	-	?	66.1	66.7
WSGCN (IRN)	ICME2021	ResNet50	ResNet101 DeepLabv2	I	-	I	66.7	68.8
AdvCAM	CVPR2021	ResNet-50	ResNet101 DeepLabv2	I	-	I	68.1	68.0
WSGCN (IRN)	ICME2021	ResNet50	ResNet101 DeepLabv2	I	-	I+C	68.7	69.3

1.2. Results on MS-COCO dataset

TODO

2. Paper List

2.1. supervised by image tags (I)

2016

• SEC: "Seed, expand and constrain: Three principles for weakly-supervised image segmentation" ECCV2016

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
• Fan et al.: "Associating inter-image salient instances for weakly supervised semantic segmentation" ECCV2018

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

• Method: "" 2019

2020

• RRM: "Reliability Does Matter An End-to-End Weakly Supervised Semantic Segmentation Approach" AAAI2020
• IAL: "Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning" IJCV2020
• SEAM: "Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation" CVPR2020
• 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

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
• WSGCN: "Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks" ICME2021
• Method: "" 2021

2.2. Supervised by bounding box (B)

2015

• WSSL: "Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation" ICCV2015

2019

• Song et al.: "Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation" CVPR2019

2021

• BBAM: "BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation" CVPR2021
• Oh et al.: "Ba ckground-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation" CVPR2021
• SPML: "Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning" ICLR2021

2.3. Supervised by scribble (S)

2018

• NormalCut : "Normalized cut loss for weakly-supervised cnn segmentation" CVPR2018
• KernelCut : "On regularized losses for weakly-supervised cnn segmentation" ECCV2018

2019

• BPG: "Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach" IJCAI2019

2020

• Method: "" 2020

2021

• SPML: "Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning" ICLR2021

2.4. Supervised by point (P)

• 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

MS COCO