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IEEE International Conference on Multimedia and Expo (ICME)

作者： Sun, Guoying Yang, Meng Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9781665468916

Single-stage weakly-supervised semantic segmentation (WSSS) with image-level labels has become a new research hotspot in the community for its lower cost and higher training efficiency. However, the pseudo label of WSSS generally suffers from somewhat noise, which limits the segmentation performance. In this paper, to explore the integral foreground activation, we propose the Channel Suppression (CS) module for preventing only activating the most discriminative regions, thereby improving the initial pseudo labels. To rectify the incorrect prediction, we explore the Self-Attention Prediction Correction (SAPC) module, which adaptively generates the category-wise prediction rectification weights. After extensive experiments, the proposed efficient single-stage framework achieves excellent performance with 67.6% mIoU and 39.9% mIoU on PASCAL VOC 2012 and MS COCO 2014 datasets, significantly exceeding several recent single-stage methods.

关键词： weakly-supervised semantic segmentation image-level label single-stage

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Global Consistency Enhancement Network for weakly-supervised semantic segmentation 6th

Global Consistency Enhancement Network for Weakly-Supervised...

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6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Jiang, Le Yang, Xinhao Ma, Liyan Li, Zhenglin Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Sch Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9789819985456;9789819985463

Generation methods for reliable class activation maps (CAMs) are essential for weakly-supervised semantic segmentation. These methods usually face the challenge of incomplete and inaccurate CAMs due to intra-class inconsistency of final features and inappropriate use of deep-level ones. To alleviate these issues, we propose the Global Consistency Enhancement Network (GCENet) that consists of Middle-level feature Auxiliary Module (MAM), Intra-class Consistency Enhancement Module (ICEM), and Critical Region Suppression Module (CRSM). Specifically, MAM uses middle-level features which carry clearer edges information and details to enhance output features. Then, for the problem of incomplete class activation maps caused by the high variance of local context of the image, ICEM is proposed to enhance the representation of features. It takes into account the intra-class global consistency and the local particularity. Furthermore, CRSM is proposed to solve the problem of excessive CAMs caused by the over-activation of features. It activates the low-discriminative regions appropriately, thus improving the quality of class activation maps. Through our comprehensive experiments, our method outperforms all other competitors and well demonstrates its effectiveness on the PASCAL VOC2012 dataset.

关键词： weakly-supervised semantic segmentation semantic segmentation Intra-class consistency

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Deconfounded multi-organ weakly-supervised semantic segmentation via causal intervention

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INFORMATION FUSION 2024年 108卷

作者： Chen, Kaitao Sun, Shiliang Du, Youtian East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian 710049 Peoples R China

In weakly-supervised semantic segmentation, obtaining the class activation maps for pseudo masks is crucial. Since multiple organs appear in the same medical image, it is reasonable to obtain the activation maps of each organ by the organ-level features instead of the image-level features. The image-level features are decomposed into the organ-level features, yet the prior anatomical knowledge makes a spurious association between the image-level and organ-level features. To this end, we apply the causal intervention to cut off the spurious association and propose a novel deconfounded multi-organ weakly-supervised semantic segmentation (DeMos) method. Based on the original class activation mapping (CAM) method, the model is retrained to learn the deconfounded features of each organ via cross-attention, and we approximate the expectation of the intervention instead of the traditional likelihood. When the model converges, we extract the activation maps by CAM. Our method not only generates high-quality pseudo masks on the CHAOS, ACDC and ProMRI datasets, but is also applicable to other CAM variants. Furthermore, with the refinement, DeMos achieves the dice similarity coefficient of 93.26% on the task of the left ventricle segmentation, which outperforms the state-of-the-art methods.

关键词： Class activation mapping weakly-supervised semantic segmentation Causal inference Deep learning

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Pseudo-Label-Free weakly supervised semantic segmentation Using Image Masking

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IEEE ACCESS 2022年 10卷 19401-19411页

作者： Kim, Sangtae Luong Trung Nguyen Shim, Kyuhong Kim, Junhan Shim, Byonghyo Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea

weakly-supervised semantic segmentation (WSSS) aims to train a semantic segmentation network using weak labels. Recent approaches generate the pseudo-label from the image-level label and then exploit it as a pixel-level supervision in the segmentation network training. A potential drawback of the conventional WSSS approaches is that the pseudo-label cannot accurately express the object regions and their classes, causing a degradation of the segmentation performance. In this paper, we propose a new WSSS technique that trains the segmentation network without relying on the pseudo-label. Key idea of the proposed approach is to train the segmentation network such that the object erased by the segmentation map is not detected by the classification network. From extensive experiments on the PASCAL VOC 2012 benchmark dataset, we demonstrate that our approach is effective in WSSS.

关键词： Image segmentation semantics Training Visualization Birds Location awareness Task analysis weakly-supervised semantic segmentation image classification visual attention saliency map

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Enhancing weakly supervised semantic segmentation through Patch-Based Refinement 13

Enhancing Weakly Supervised Semantic Segmentation through Pa...

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13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP)

作者： Tajrishi, Narges Javid Afshar, Sepehr Amini Kasaei, Shohreh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

weakly-supervised semantic segmentation (WSSS) with image-level labels, commonly uses Class Activation Maps (CAM) to generate pseudo-labels. However, Convolutional Neural Networks (CNNs), with their limited local receptive field, often struggle to identify entire object regions. Recently, the Vision Transformer (ViT) architecture has been employed instead of CNNs to capture long-range feature dependencies, by using the self-attention mechanism. Despite its advantages, ViT tends to overlook local feature details, leading to attention maps with low quality and unclear object details. This paper introduces a novel method to enhance the local details in attention maps by leveraging local patches. These local patches are selected from regions that are more likely to contain the desired objects. By effectively utilizing these local patches during the training and generation stages, the model yields more detailed attention maps. Extensive experiments were conducted on the PASCAL VOC 2012 benchmark dataset to demonstrate the efficacy of the proposed approach. The results show significant improvements (+2.6% mIoU) with minimal computational overhead, underscoring the potential of the proposed method in the field of weakly-supervised semantic segmentation.

关键词： weakly-supervised semantic segmentation Image Classification Deep Learning Vision Transformer

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Max Pooling with Vision Transformers Reconciles Class and Shape in weakly supervised semantic segmentation 17th

Max Pooling with Vision Transformers Reconciles Class and Sh...

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17th European Conference on Computer Vision (ECCV)

作者： Rossetti, Simone Zappia, Damiano Sanzari, Marta Schaerf, Marco Pirri, Fiora DeepPlants Rome Italy Sapienza DIAG Rome Italy

ISBN: (纸本)9783031200557;9783031200564

weakly supervised semantic segmentation (WSSS) research has explored many directions to improve the typical pipeline CNN plus class activation maps (CAM) plus refinements, given the image-class label as the only supervision. Though the gap with the fully supervised methods is reduced, further abating the spread seems unlikely within this framework. On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM. ViT features have been shown to retain a scene layout, and object boundaries in self-supervised learning. To confirm these findings, we prove that the advantages of transformers in self-supervised methods are further strengthened by Global Max Pooling (GMP), which can leverage patch features to negotiate pixel-label probability with class probability. This work proposes a new WSSS method dubbed ViT-PCM (ViT Patch-Class Mapping), not based on CAM. The end-to-end presented network learns with a single optimization process, refined shape and proper localization for segmentation masks. Our model outperforms the state-of-the-art on baseline pseudo-masks (BPM), where we achieve 69.3% mIoU on PascalVOC 2012 val set. We show that our approach has the least set of parameters, though obtaining higher accuracy than all other approaches. In a sentence, quantitative and qualitative results of our method reveal that ViT-PCM is an excellent alternative to CNN-CAM based architectures.

关键词： weakly-supervised semantic segmentation Vision transformers Global max pooling Image class-labels supervision

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Dual semantic-guided model for weakly-supervised zero-shot semantic segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 5443-5458页

作者： Shen, Fengli Lu, Zhe-Ming Lu, Ziqian Wang, Zonghui Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

The major obstacle in semantic segmentation is that it requires a large number of pixel-level labeled data to train an effective model. In order to reduce the cost of annotation, weakly-supervised methods use weaker labels to overcome the need for per-pixel labels, while zero-shot methods transfer the knowledge learned from seen classes to unseen classes to reduce the number of classes that need to be labeled. To further alleviate the burden of annotation, we introduce a more challenging task of weakly-supervised Zero-shot semantic segmentation (WZSS): learning models which only utilize image-level annotation of seen classes to segment images containing unseen objects. To this end, we propose a Dual semantic-Guided (DSG) model which is double guided by semantic embeddings of classes to obtain classification scores and localization maps. By ignoring the localization maps with low classification scores, our proposed framework can generate prediction segmentation masks. To improve our model's performance, we propose a simple stochastic selection on semantic embeddings during inference, which explores the difference between image-level class embeddings and pixel-level class embeddings. This simple approach increases our model's performance in terms of hIoU from 25.9 to 31.8. In addition, compared with some zero-shot semantic segmentation methods, our method delivers better results in terms of hIoU (31.8) and mIoU(u) (22.0) on the PASCAL VOC 2012 dataset with less supervision information.

关键词： weakly-supervised zero-shot semantic segmentation Zero-shot leaning weakly-supervised semantic segmentation

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Transformer Based Prototype Learning for weakly-supervised Histopathology Tissue semantic segmentation 1

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： She, Jinwen Hu, Yanxu Ma, Andy J. Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (数字)9783031442162

ISBN: (纸本)9783031442155;9783031442162

weakly-supervised semantic segmentation for computational pathology has the great potential to alleviate the time-consuming and labor-intensive burden of manual pixel-level annotations. Existing methods relying on class activation map (CAM) to localize target objects suffer from two problems. First, most CAM-based models adopt convolutional neural networks, which cannot model the long-range dependencies of dispersed tissues. Second, CAM tends to focus on the most discriminative region of the object, resulting in incomplete segmentation results. In this paper, we propose a novel Transformer based weakly-supervised model for pixel-level tissue segmentation. The proposed model is able to capture global tissue feature relations by the self-attention mechanism in Transformer. For the issue of incomplete segmentation in CAM, we propose a patch-token prototype self-supervised learning approach to obtain more complete localization maps. Additionally, we introduce a self-refinement mechanism to dampen the falsely activated regions in the initial localization map. Extensive experiments on two histopathology datasets demonstrate that our proposed model achieves the state-of-the-art performance compared with other weakly-supervised methods.

关键词： Histopathology image weakly-supervised semantic segmentation Transformer Prototype learning

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SSA: semantic structure aware inference on CNN networks for weakly pixel-wise dense predictions without cost

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Frontiers of Computer Science 2025年第2期19卷 1-10页

作者： Yanpeng SUN Zechao LI School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210014China

The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps(CAMs)to generate pseudo masks as ***,existing methods often incorporate trainable modules to expand the immature class activation maps,which can result in significant computational overhead and complicate the training *** this work,we investigate the semantic structure information concealed within the CNN network,and propose a semantic structure aware inference(SSA)method that utilizes this information to obtain high-quality CAM without any additional training ***,the semantic structure modeling module(SSM)is first proposed to generate the classagnostic semantic correlation representation,where each item denotes the affinity degree between one category of objects and all the ***,the immature CAM are refined through a dot product operation that utilizes semantic structure ***,the polished CAMs from different backbone stages are fused as the *** advantage of SSA lies in its parameter-free nature and the absence of additional training costs,which makes it suitable for various weakly supervised pixel-dense prediction *** conducted extensive experiments on weakly supervised object localization and weakly supervised semantic segmentation,and the results confirm the effectiveness of SSA.

关键词： class attention maps semantic structure weaklysupervised object localization weakly-supervised semantic segmentation

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Local optimization cropping and boundary enhancement for end-to-end weakly-supervised segmentation network

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COMPUTER VISION AND IMAGE UNDERSTANDING 2025年 251卷

作者： Wang, Weizheng Zeng, Chao Wang, Haonan Zhou, Lei Changsha Univ Sci & Technol Changsha 410000 Peoples R China

In recent years, the performance of weakly-supervised semantic segmentation(WSSS) has significantly increased. It usually employs image-level labels to generate Class Activation Map (CAM) for producing pseudo-labels, which greatly reduces the cost of annotation. Since CNN cannot fully identify object regions, researchers found that Vision Transformers (ViT) can complement the deficiencies of CNN by better extracting global contextual information. However, ViT also introduces the problem of over-smoothing. Great progress has been made in recent years to solve the over-smoothing problem, yet two issues remain. The first issue is that the high-confidence regions in the network-generated CAM still contain areas irrelevant to the class. The second issue is the inaccuracy of CAM boundaries, which contain a small portion of background regions. As we know, the precision of label boundaries is closely tied to excellent segmentation performance. In this work, to address the first issue, we propose a local optimized cropping module (LOC). By randomly cropping selected regions, we allow the local class tokens to be contrasted with the global class tokens. This method facilitates enhanced consistency between local and global representations. To address the second issue, we design a boundary enhancement module (BE) that utilizes an erasing strategy to re-train the image, increasing the network's extraction of boundary information and greatly improving the accuracy of CAM boundaries, thereby enhancing the quality of pseudo labels. Experiments on the PASCAL VOC dataset show that the performance of our proposed LOC-BE Net outperforms multi-stage methods and is competitive with end-to-end methods. On the PASCAL VOC dataset, our method achieves a CAM mIoU of 74.2% and a segmentation mIoU of 73.1%. On the COCO2014 dataset, our method achieves a CAM mIoU of 43.8% and a segmentation mIoU of 43.4%. Our code has been open sourced: https://***/whn786/LOC-BE/tree/main.

关键词： Deep learning weakly-supervised semantic segmentation Computer vision Single-stage Boundary enhancement Local optimization cropping

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