State-of-the-art approaches for semantic segmentation rely on deep convolutional neural networks trained on fully annotated datasets, that have been shown to be notoriously expensive to collect, both in terms of time and money. To remedy this situation, weakly supervised methods leverage other forms of supervision that require substantially less annotation effort, but they typically present an inability to predict precise object boundaries due to approximate nature of the supervisory signals in those regions. While great progress has been made in improving the performance, many of these weakly supervised methods are highly tailored to their own specific settings. This raises challenges in reusing algorithms and making steady progress. In this paper, we intentionally avoid such practices when tackling weakly supervised semantic segmentation. In particular, we train standard neural networks with partial cross-entropy loss function for the labeled pixels and our proposed Gated CRF loss for the unlabeled pixels. The Gated CRF loss is designed to deliver several important assets: 1) it enables flexibility in the kernel construction to mask out influence from undesired pixel positions; 2) it offloads learning contextual relations to CNN and concentrates on semantic boundaries; 3) it does not rely on high-dimensional filtering and thus has a simple implementation. Throughout the paper we present the advantages of the loss function, analyze several aspects of weakly supervised training, and show that our `purist' approach achieves state-of-the-art performance for both click-based and scribble-based annotations.
University of Waterloo（滑铁卢大学）；Disney Research（迪士尼研究院）；École de Technologie Supérieure, University of Quebec（魁北克大学高等技术学院）
Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segmentation currently dominated by methods mimicking full supervision via "fake" fully-labeled training masks (proposals) generated from available partial input. To obtain such full masks the typical methods explicitly use standard regularization techniques for "shallow" segmentation, e.g. graph cuts or dense CRFs. In contrast, we integrate such standard regularizers directly into the loss functions over partial input. Thi…
In this work, the case of semantic segmentation on a small image dataset (simulated by 1000 randomly selected images from PASCAL VOC 2012), where only weak supervision signals (scribbles from user interaction) are available is studied. Especially, to tackle the problem of limited data annotations in image segmentation, transferring different pre-trained models and CRF based methods are applied to enhance the segmentation performance. To this end, RotNet, DeeperCluster, and Semi&Weakly Supervised Learning (SWSL) pre-trained models are transferred and finetuned in a DeepLab-v2 baseline, and dens…
Data61-CSIRO（澳大利亚联邦科学与工业研究组织数据61）；Australian National University（澳大利亚国立大学）；ShanghaiTech University（上海科技大学）；The University of Western Australia（西澳大学）；Murdoch University（莫道克大学）
Conventional change detection methods require a large number of images to learn background models or depend on tedious pixel-level labeling by humans. In this paper, we present a weakly supervised approach that needs only image-level labels to simultaneously detect and localize changes in a pair of images. To this end, we employ a deep neural network with DAG topology to learn patterns of change from image-level labeled training data. On top of the initial CNN activations, we define a CRF model to incorporate the local differences and context with the dense connections between individual pixel…
Generating training sets for deep convolutional neural networks (DCNNs) is a bottleneck for modern real-world applications. This is a demanding task for applications where annotating training data is costly, such as in semantic segmentation. In the literature, there is still a gap between the performance achieved by a network trained on full and on weak annotations. In this paper, we establish a strategy to measure this gap and to identify the ingredients necessary to reduce it. On scribbles, we establish new state-of-the-art results: we obtain a mIoU of 75.6% without, and 75.7% with CRF post-…
Commonwealth Science and Industrial Research Organization (CSIRO)（澳大利亚联邦科学与工业研究组织）；EPFL（瑞士洛桑联邦理工学院）；The Australian National University (ANU)（澳大利亚国立大学）
Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require training pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we p…
Semantic video segmentation is challenging due to the sheer amount of data that needs to be processed and labeled in order to construct accurate models. In this paper we present a deep, end-to-end trainable methodology to video segmentation that is capable of leveraging information present in unlabeled data in order to improve semantic estimates. Our model combines a convolutional architecture and a spatio-temporal transformer recurrent layer that are able to temporally propagate labeling information by means of optical flow, adaptively gated based on its locally estimated uncertainty. The flo…
The University of Hong Kong（香港大学）；Deepwise AI Lab（深度智耀人工智能实验室）
Given a training dataset composed of images and corresponding category labels, deep convolutional neural networks show a strong ability in mining discriminative parts for image classification. However, deep convolutional neural networks trained with image level labels only tend to focus on the most discriminative parts while missing other object parts, which could provide complementary information. In this paper, we approach this problem from a different perspective. We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts de…
Hong Kong University of Science and Technology（香港科技大学）；INRIA（法国国家信息与自动化研究所）；University of Sydney（悉尼大学）；Chinese University of Hong Kong（香港中文大学）；University of Trento（特伦托大学）
Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention netwo…
Recently, several weakly supervised learning methods have been devoted to utilize bounding box supervision for training deep semantic segmentation models. Most existing methods usually leverage the generic proposal generators (e.g., dense CRF and MCG) to produce enhanced segmentation masks for further training segmentation models. These proposal generators, however, are generic and not specifically designed for box-supervised semantic segmentation, thereby leaving some leeway for improving segmentation performance. In this paper, we aim at seeking for a more accurate learning-based class-agnos…
Semantic image segmentation, which becomes one of the key applications in image processing and computer vision domain, has been used in multiple domains such as medical area and intelligent transportation. Lots of benchmark datasets are released for researchers to verify their algorithms. Semantic segmentation has been studied for many years. Since the emergence of Deep Neural Network (DNN), segmentation has made a tremendous progress. In this paper, we divide semantic image segmentation methods into two categories: traditional and recent DNN method. Firstly, we briefly summarize the tradition…
Australian National University（澳大利亚国立大学）；ACRV（澳大利亚机器人视觉中心）；Data61（数据61）；University of Technology Sydney（悉尼科技大学）；Northwestern Polytechnical University（西北工业大学）
Compared with laborious pixel-wise dense labeling, it is much easier to label data by scribbles, which only costs 1$\sim$2 seconds to label one image. However, using scribble labels to learn salient object detection has not been explored. In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations. In doing so, we first relabel an existing large-scale salient object detection dataset with scribbles, namely S-DUTS dataset. Since object structure and detail information is not identified by scribbles, directly training with scribble labels …
Indian Institute of Science（印度科学研究所）；Department of Computer Science and Automation（计算机科学与自动化系）
Deep convolutional networks have achieved the state-of-the-art for semantic image segmentation tasks. However, training these networks requires access to densely labeled images, which are known to be very expensive to obtain. On the other hand, the web provides an almost unlimited source of images annotated at the image level. How can one utilize this much larger weakly annotated set for tasks that require dense labeling? Prior work often relied on localization cues, such as saliency maps, objectness priors, bounding boxes etc., to address this challenging problem. In this paper, we propose a …
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation
AI VIETNAM Lab（AI越南实验室）；Washington University School of Medicine（华盛顿大学医学院）；Jeonbuk National University（全北国立大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Oregon State University（俄勒冈州立大学）；Amazon.com Services, Inc（亚马逊服务公司）
This paper is about labeling video frames with action classes under weak supervision in training, where we have access to a temporal ordering of actions, but their start and end frames in training videos are unknown. Following prior work, we use an HMM grounded on a Gated Recurrent Unit (GRU) for frame labeling. Our key contribution is a new constrained discriminative forward loss (CDFL) that we use for training the HMM and GRU under weak supervision. While prior work typically estimates the loss on a single, inferred video segmentation, our CDFL discriminates between the energy of all valid a…
Weakly supervised multimodal segmentation of acoustic borehole images with depth-aware cross-attention
Federal University of Bahia, Institute of Geosciences, Department of Geophysics（巴伊亚联邦大学，地质科学学院，地球物理学系）；Grupo de Estudos e Aplicação de Inteligência Artificial em Geofísica (GAIA)（地质物理中人工智能研究与应用小组）
University at Buffalo, The State University of New York（纽约州立大学布法罗分校）
The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually labeled masks, which is highly time-consuming and needs domain expertise. Weakly supervised learning with scribbles provides a good trade-off between model accuracy and the effort of manual labeling. However, for segmenting the hierarchical brain tumor structures, manually labeling scribbles for each substructure could still be demanding. In this paper, we use only two kinds of weak labels, i.e., scribbles on whole tumor …
C2RM-Seg: Causal Counterfactual Reasoning with Structural-Semantic Priors for Weakly Supervised Histopathological Tissue Segmentation
Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology（广西图像图形智能处理重点实验室，桂林电子科技大学）；International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services, Guilin University of Electronic Technology（时空信息与智能定位服务国际联合研究实验室，桂林电子科技大学）；School of Computer Science and Information Security, Guilin University of Electronic Technology（桂林电子科技大学计算机与信息安全学院）
University of Connecticut（康涅狄格大学）；University of Connecticut Health Center（康涅狄格大学健康中心）
Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. Howev…
Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；Beijing Institute of Remote Sensing Information（遥感信息北京市研究院）
