Saliency Guidance and Expansion Suppression on PuzzleCAM for Weakly Supervised Semantic Segmentation

Abstract

The semantic segmentation model usually provides pixel-wise category prediction for images. However, a massive amount of pixel-wise annotation images is required for model training, which is time-consuming and labor-intensive. An image-level categorical annotation is recently popular and attempted to overcome the above issue in this work. This is also termed weakly supervised semantic segmentation, and the general framework aims to generate pseudo masks with class activation mapping. This can be learned through classification tasks that focus on explicit features. Some major issues in these approaches are as follows: (1) Excessive attention on the specific area; (2) for some objects, the detected range is beyond the boundary, and (3) the smooth areas or minor color gradients along the object are difficult to categorize. All these problems are comprehensively addressed in this work, mainly to overcome the importance of overly focusing on significant features. The suppression expansion module is used to diminish the centralized features and to expand the attention view. Moreover, to tackle the misclassification problem, the saliency-guided module is adopted to assist in learning regional information. It limits the object area effectively while simultaneously resolving the challenge of internal color smoothing. Experimental results show that the pseudo masks generated by the proposed network can achieve 76.0%, 73.3%, and 73.5% in mIoU with the PASCAL VOC 2012 train, validation, and test set, respectively, and outperform the state-of-the-art methods.

1. Introduction

Supervised semantic segmentation [1,2] comprises deep neural networks that perform intensive regional feature learning. The network provides point-by-point category prediction for all image pixels. The data preparation for such training tasks is tedious, and requires enormous labor. For instance, let us consider a street scene image [3], containing many elements such as vehicles, people, houses, street lights, roads, and other trivial objects. The manual annotations need to be performed to a fine-grain level for such images, and it is heavily time-consuming and expensive to develop a large-scale dataset. This limits the generalization capabilities of the semantic segmentation models. To address the above issue, weakly supervised approaches have been investigated, and the motivation is to use annotations that are weaker than that of the pixel-level labels.

Among the various types of weak annotations, image-level class labels have been widely used for their promising results, and many large-scale image datasets in relevance are already available. In general, the weakly supervised semantic segmentation tasks [4,5,6,7] use the class activation map (CAM) [8] as the base object location. The classifier can effectively determine the object’s existence in the image and localize well to the object of interest. The resultant attention features are mapped to the image, producing a pixel-level pseudo-label. The generation of such pseudo masks may cause several problems:

(1)

Finding the location of objects by classification model focuses on features that are well-defined and frequently appear on the same object. Mostly, the predictions are based on the well-localized and fine-grain regions, and hence the complete object may not be properly localized and segmented.

(2)

When the focus is on the scenes, such as the image of a boat on the water, an airplane in the air, etc., the background is sometimes misjudged as a single object in the mapping. This results in pseudo masks generated even beyond the object areas.

(3)

Smooth color regions in the object are hard to label using the general classifiers.

To overcome the above limitations, a new architecture is proposed to generate pixel-wise pseudo labels based on a classification scheme. The main contributions of this study are as follows.

• The ideal Suppression Module (SUPM) is proposed and integrated to enlarge the object’s attention.
• Saliency Map Guided Module (SMGM) is developed to resolve the issues in smooth color regions.
• Multi-Task Learning framework is adopted to jointly train the model for classification and segmentation task.

The overall manuscript is organized as follows. Section 2 provides a brief review of weakly supervised semantic segmentation models. Section 3 provides a detailed description of the proposed method. Section 4 focuses on detailed experiments to validate the performance of the proposed method compared to the state-of-the-art works. Finally, Section 5 draws a conclusion.

2. Related Works

The weakly supervision uses the more easily available annotation data, and the pseudo-labels are generated for subsequent training. The basic annotation methods that are commonly seen in weakly supervised semantic analysis tasks are bounding boxes [9,10,11], scribbles [12,13], and image-level annotation [5,6,14,15,16,17,18,19,20,21,22,23,24,25,26]. Some important works in the weakly supervised semantic segmentation is presented in

Table 1. Literature Review.

Reference	Methodology	Limitations
G. Papandreou et al. [9]	In this work, weakly annotated data or image-level labels are used for training. The method is based on Expectation-Maximization (EM) strategy and can learn from a few strongly labelled and many weakly labelled data.	Performs with limited accuracy in case of images with overlapping class objects.
D. Lin et al. [13]	An alternate approach is proposed, which can significantly reduce the annotation time using only scribbles. A graphical model is proposed to learn from scribbled pixels and then propagate to the unmarked pixels.	Though the approach reduces manual annotation time, it cannot be completely eliminated.
A. Khoreva et al. [11]	The method involves recursive training of convnets and tools like GrabCut to generate the training label from the bounding boxes.	Limited to fully and semi-supervised approaches, and weakly supervised cannot be performed.
P. Vernaza et al. [12]	In this work, a label-propagation algorithm is proposed using random walk-hitting probabilities. The method can be trained using sparse image labeling.	The approach is tested on VOC2012 and is not applicable to the bounding box and weakly supervised training.
S. Jo et al. [7]	A new approach to generated pseudo-labels is proposed using the puzzle module, and two regularization terms termed Puzzle-CAM.	The method has problems such as the entire object may not be properly segmented, and sometimes the background pixels are considered as foreground.
J. Lee et al. [17]	The Fickle-Net explores the multiple combinations of the feature map locations and uses them to obtain the activation scores for superior image classification.	Though the method produces accurate segmentation, the detected object boundaries are slightly eroded compared to the ground truth.
B. Kim et al. [21]	In the Discriminative Region Suppression (DRS) [21] architecture, the focus points with high values on the feature map are directly suppressed. This increases the training burden and extracts deep features. The mechanism targets the prominent feature and then the extent to other feature areas to obtain a broader perspective of objects.	The methods [21,26] failure cases are in long-range connected objects such as bird wings. Some object regions may get eroded, and the segmented objects have too many missing pixel.
J. Ahn [26]	The method initially aims to obtain pseudo-instance segmentation labels, which can be used for supervised model training. In addition, a new model based on inter-pixel relationship (IRNet) can detect approximate individual instances and detects boundary between different object classes.
S. H. Jo [23]	In the latest, an improved method termed RecurSeed is proposed, which recursively reduces non and false-detections. A novel data augmentation technique termed EdgePredictMix is also proposed to find optimal edge junctions which can minimize boundary errors.	There is a scope for improvement in terms of object boundaries and reducing the detection of unwanted background regions.

.

The most common approach to generate pseudo masks uses image-level annotations, which perform object identification through classification networks. The distinctive features are generally visualized using Class Activation Maps (CAM) tools. The classification prediction results can backward search the attention features of its objects with global average pooling layers for visualization. The original CAMs [8] method has a drawback that tends to focus on a small object area. The Puzzle-CAM [7] is proposed to enhance the category mapping using the image slice. This method improves the consistency of the object’s area of interest by merging and making the range of class features in more comprehensive. Among the existing work, the current work is built on the base model of the Puzzle-CAM method, and many novel techniques are built on its top to arrive at state-of-the-art performance.

Firstly, the suppression control mechanism is developed to improvise the object recognition area. Highly discriminative features are critical for classification tasks, yet these features are often a part of the object, and the entire object may not be covered. Suppression controls are used to expand the field of view of the feature attention to spread the attention from the high-recognition area to the surrounding neighborhood.

Secondly, a saliency guidance module is developed for improvised category mapping classification networks. It is often possible for objects to co-occur with the corresponding backgrounds, and this leads to an erroneous dispersion of attention to the external range of the objects from the region where the features are distinctive. To obtain more precise object localization, saliency assistance is used to provide coarse foreground object inspection. This prevents the spread of attention to unexpected background regions.

Finally, the Multi-Task Learning (MTL) framework is adopted to improve the model generalization through shared representations among related tasks. The proposed approach uses MTL to enhance semantic segmentation by jointly training the model for classification and segmentation tasks. The overall architechture of the proposed method is shown in Figure 1 and a detailed description is provided in the next section.

3. Proposed Method

In this study, a novel architecture is proposed for pseudo masks generation. The proposed method contains three main components: (1) Classification, (2) Suppression Module (SUPM), and (3) Saliency Map Guided Module (SMGM). Figure 1 shows the overall framework of the proposed architecture. The first phase of the proposed architecture comprises of a classification module, generating class activation as the first step to obtain pseudo masks. The second part is the saliency map guidance, which is used to assist the classification mapping with global object information.

The foremost process of classification to obtain the category mapping consists of two steps: To start with, the full-image version is initially passed through the classification network to predict the object in the image. Simultaneously, the image is cropped into four equal parts and also fed into the classification module. The classification activation map of the complete path and the split path are compared, and the result shows that the difference is reduced. It can be seen from Figure 1 that the field of attention is expanded in the cropped version in comparison to the full image. Consequently, the classification map has the complete coverage over the object of interest

Figure 2 shows the backbone of the classification architecture. The standard ResNet backbone [18] is used, which consists of five levels of feature extraction stages from Enc1 to Enc5, and the high-dimensional features are extracted in the image. The SUPM is connected after each extraction layer to suppress the most concerned feature area on the feature map. Moreover, the suppression control also expands the attention to the surrounding objects of concern, and helps to learn more subtle features. After the feature extraction and suppression in the convolution layer, the extracted feature map is optimized using the classification and reconstruction loss function. As in [25], the global average pooling is used as it can make the network more stable, and achieve better results than that using a fully connected layer. Global average pooling effectively reflects the classification results on the class feature map, and finally obtains the activation map and image category type for the object of interest.

The purpose of the SUPM is to restrain the high recognition features and make recognition more difficult. This helps to learn more subtle and distinctive feature regions, and also facilitates mask diffusion. In addition, it can improve the attention from a small localized area to the neighboring areas in the target object. The overall procedure to obtain suppression value is as follows. Let us assume, intermediate and suppressed feature maps are $X\in R^{H\times W\times C}$ and $\widehat{X}\in R^{H\times W\times C}$. The maximum and minimum feature values of each channel are computed as in Equations (1) and (2).

$$X_{max}=max\left(X\right)\to Suppress target∕∕X_{max}\in R^{1\times 1\times C}$$

(1)

$$X_{avg}=avg\left(X\right)\to Control reference∕∕X_{avg}\in R^{1\times 1\times C}$$

(2)

Further, the controller and suppressor module are applied, and then final value $\widehat{X}$ is computed as in Equations (3)–(5).

$$controller=sigmoid\left(fc\left(X_{avg}\right)\right)∕∕controller\in {\left[0, 1\right]}^{1\times 1\times C}$$

(3)

$$suppressor=X_{max}·controller∕∕suppressor\in R^{1\times 1\times C}$$

(4)

$$\widehat{X}=\min \left(X,suppressor\right)∕∕suppression$$

(5)

In the target extraction stage, the $X_{max}$ corresponds to the maximum feature value of each channel in the feature map. As shown in Figure 3, the second stage is the output of the suppression control base, indicated as $X_{avg}.$ It is the average value of each channel’s feature points, which is used as the reference baseline of the controller. The $X_{avg}$ of each channel is passed through the fully connected layer and also sigmoid function to learn the suppression weight of each channel, and the normalization is performed. Subsequently, the final suppression is performed to obtain the suppression target $X_{max}$, and is multiplied by the controller. When a feature value in this layer is larger than the inhibition value, it is constrained to the inhibition value size to achieve the inhibition effect.

In the Saliency Map Guided Module (SMGM), the saliency map is used to guide and assist the classification network in a multi-task learning approach. Based on the autoencoder method, the encoder is responsible for feature extraction, and the decoder is responsible for decoding the extracted features. The classification network performs feature extraction as the encoder operation, and guides the extracted features into the SMGM decoder. The module assists the classification activation map to keep the objects within their boundaries. The region learning works simultaneously in the foreground, providing more feature information on the parts of the objects.

In the classification task, as the dataset image contains multiple objects, the multi-label approach is used to learn the prediction result. The main network contains the full-image path and the cropped-merged image path, and respectively provide the classification loss, $Los{s}_{cl}$ and $Los{s}_{pcl}$, to predict objects in images as in Equations (6) and (7).

$$Los{s}_{cl}=-{\sum }_{i=1}^c{y}_i\log \left(sigmoid\left({\widehat{y}}_i^s\right)\right),$$

(6)

$$Los{s}_{pcl}=-{\sum }_{i=1}^c{y}_i\log \left(sigmoid\left({\widehat{y}}_i^{re}\right)\right).$$

(7)

In reconstruction learning, ${\widehat{y}}_{ic}^s$ is the predicted area for each category of the full-image, and ${\widehat{y}}_{ic}^{re}$ is the predicted area for each category after merging the cropped images. Due to the necessity of reducing the difference between the two predictions, the Mean Absolute Error (MAE) loss function is used to reduce the absolute difference between the two predicted images for each pixel as shown in Equation (8).

$$Los{s}_{re}=\frac{1}{n}{\sum }_{i=1}^n\left|{\widehat{y}}_{i1}^s-{\widehat{y}}_{i1}^{re}\right|+\left|{\widehat{y}}_{i2}^s-{\widehat{y}}_{i2}^{re}\right|+\cdots +\left|{\widehat{y}}_{ic}^s-{\widehat{y}}_{ic}^{re}\right|.$$

(8)

For saliency loss, the learning target of the saliency map is $y_{i1}$, and the feature decoding reduction result is ${\widehat{y}}_{i1}$. To accelerate the training process, The Mean Square Error (MSE) loss is used as in Equation (9) to learn the pixel difference between each other.

$$Los{s}_{sal}=\frac{1}{n}{\sum }_{i=1}^n{\left(y_{i1}-{\widehat{y}}_{i1}\right)}^2.$$

(9)

In the total loss $Los{s}_{all}$ as provided in Equation (10), the ${\lambda }_1$ is weight for the restoration loss of weight, and ${\lambda }_2$ is the weight for saliency loss.

$$Los{s}_{all}=Los{s}_{cl}+Los{s}_{pcl}+{\lambda }_{1}Los{s}_{re}+{\lambda }_{2}Los{s}_{sal}$$

(10)

4. Experiment Result

In this section, the public dataset PASCAL VOC 2012 is utilized for comprehensive experimentation and analysis. The results are compared against the former state-of-the-art weakly supervised segmentation algorithms [7,17,23,24,25,26]. In this study, the mean Intersection over Union (mIoU) is adopted for the evaluation of the overall performance.

4.1. Datasets

4.1.1. PASCAL VOC 2012

PASCAL VOC dataset [27] is also known as the PASCAL Visual Objected Classes which includes many machine vision tasks such as classification, object detection, segmentation, action classification, etc. In the weakly supervised semantic segmentation task, we use image-level labels for pseudo mask generation and pixel-level labels for validating semantic segmentation results.

4.1.2. Augmented Dataset

The augmented dataset used in this paper is derived from the SBD dataset [28]. The images in SBD dataset are from PASCAL VOC 2011. It is used in the classification task to increase object diversity in the training phase.

4.1.3. Saliency Map

The salient images are used as a guide for salient feature learning in the classification tasks. Subsequently, the PASCAL VOC 2012 training dataset and augmented dataset are applied to PoolNet [29] to obtain the salient maps.

4.2. Training

For training, the computational hardware platform consists of the following components. CPU: Intel(R) Xeon(R) Gold 5218 CPU 2.30 GHz; GPU: 1 × NVIDIA A100 (80 GB); RAM: 8 × DDR4 2666 (64 GB), and operating system: Ubuntu 18.04 LTS. The images are randomly re-scaled in the range of [320, 640] and then cropped to 512 × 512. Thus, we adopted multi-scale and horizontal flips to generate pseudo masks.

4.3. Ablation Test

4.3.1. Module Ablation Experiment

To verify the proposed class-activated classification network, the SUPM and SMGM are analyzed using the ResNet50 backbone.

Table 2. Testing with SUPM AND SMGM on PASCAL VOC2012 Training Dataset of Pseudo Masks in MIOU (%).

Backbone	SUPM	SMGM	mIoU (%)
ResNet50			47.63
	V		50.52
		V	49.57
	V	V	52.32

shows that the combination of SUPM and SMGM yields the best results. Figure 4 shows the visualization results of class activation with the two modules, and it is found that when the SUPM and SMGM are used together, there is an object diffusion in the area of interest along the dining table and the local restriction in the area of interest of the airplane. Thus, using SUPM and SMGM together can generate the best-quality pseudo-labels for subsequent semantic segmentation task training.

4.3.2. Loss Function

In the proposed class mapping network, the total loss is $Los{s}_{all}.$ The $Los{s}_{re}$ is the difference between the complete image region of interest and the restored segmented image, and $Los{s}_{sal}$ is the region loss assisted by the saliency map. The pseudo labels generated on the PASCAL VOC 2012 training set are evaluated by mIoU using different loss functions in the same case.

Table 3. Testing loss function weights for generating pseudo masks in PASCAL VOC 2012 training set in MIOU (%).

		${\mathit{\lambda }}_2$
		1	2	3	4
${\lambda }_1$	1	51.74	52.42	52.90	52.81
	2	52.30	53.20	53.29	52.95
	3	51.82	52.87	52.99	52.82

shows that $Los{s}_{re}$ with mean absolute error loss and $Los{s}_{sal}$ with mean square is best suited for pseudo mask generation. The reason is that $Los{s}_{re}$ learns the difference between the area of concern of the complete image and the segmented images. The sensitivity of the mean square error loss to the deviation value is too strong, and thus the predicted changes between the two are harmful to each other, making it more difficult to stabilize. In contrast, the sensitivity of mean absolute error is lower, and the two predicted results can learn better from each other.

4.3.3. Backbone Selection

In this study, the same backbone networks as that of the baseline PuzzleCAM [7] such as ResNet [30], and ResNeSt [18] are used for fair comparison.

Table 4. Testing different backbones for generating pseudo masks in PASCAL VOC 2012 testing set in MIOU (%).

Method	Backbone	CAM	CAM + RW	CAM + RW + dCRF
PuzzleCAM [7]	ResNeSt101	61.85	71.92	72.46
Proposed Method	ResNet50	53.29	64.01	64.38
	ResNeSt50	60.09	68.50	68.75
	ResNeSt101	64.19	75.67	76.00

shows the experimental results, in which Random Walk (RW) is adopted from affininty-net [5] and dense conditional random field (dCRF) is adopted from [19]. The proposed method in this paper uses ResNeSt101 as the backbone and further enhancement through the random walk and dense conditional random field to generate pseudo masks.

4.3.4. Comparison

The experimental results yielded the best-performing architecture for the generation of pixel-level pseudo labels, which were compared with previous techniques with the PASCAL VOC 2012 dataset. In

Table 5. Comparison of pseudo masks generation results with other existing method in MIOU (%).

Method	Backbone	Sup.	mIoU (%)
EPS [17] (2021)	ResNet38	I, S	71.6
PuzzleCAM [7] (2021)	ResNeSt269	I	74.7
L2G [22] (2022)	ResNet38	I, S	71.9
RSCM [23] (2022-SOTA)	ResNet50	I	75.5
Proposed Method	ResNeSt101	I, S	76.0

, Sup. represents the supervised approach, in which I is the training image with image-level labels and S is the training image with saliency maps. According to the comparison results in Table 5, the proposed method achieved the best results on the pseudo masks generated from the public dataset of 76.0% in terms of the mIoU. Figure 5 shows the pseudo masks generated from the proposed method, and the locality of the objects in the pseudo masks are well represented.

The generated pseudo masks are on PASCAL VOC 2012 training dataset and the augmented dataset for semantic segmentation training with deeplabv3+ [31], the final results were compared with other methods in PASCAL VOC 2012 validation and test datasets and evaluated with mIoU. The results are shown in

Table 6. Comparison of semantic segmentation results against the state-of-the-art methods with MIOU (%).

Method	Backbone	Sup.	Val	Test
SG-SAN [26] (2019)	ResNet101	I, S	67.1	67.2
DRS [21] (2021)	ResNet101	I, S	71.2	71.4
PuzzleCAM [7] (2021)	ResNeSt269	I	71.9	72.2
RSCM [23] (2022-SOTA)	ResNet101	I	72.4	73.2
Proposed Method	ResNeSt101	I, S	73.3	73.5

. As it can be seen that the proposed weakly supervised semantic segmentation achieve the best performance with improvement 0.9% over the existing state-of-the-art RSCM [23] for the validation dataset, and 0.3% for the testing dataset, respectively. Figure 6 shows the practical segmentation results compared with ground truth.

5. Conclusions

In weakly supervised semantic segmentation tasks, classification networks are often used to learn the regions of interest for each category, and generate pixel-level pseudo masks. In this paper, the Suppression Module (SUPM) is adopted to suppress strong points of interest and to diffuse concern to the surrounding area. To resolve the problem of misclassifying scenes as objects, the Saliency Map Guidance Module (SMGM) is designed and integrated, which uses a multi-task learning mechanism to help the classification network obtain the background area of objects. Compared with the state-of-the-art methods, the classification network framework designed in this study generates 76.0% pseudo masks on the PASCAL VOC 2012 training set and 73.3% and 73.5% on the PASCAL VOC 2012 validation and testing sets for training semantic segmentation networks with pseudo masks, and these are examined with superior performance compared to the state-of-the-art methods.