Res2Net: A New Multi-scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure

Salient Object Detection for Images Taken by People With Vision Impairments

Salient object detection is the task of producing a binary mask for an image that deciphers which pixels belong to the foreground object versus background. We introduce a new salient object detection dataset using images taken by people who are visually impaired who were seeking to better understand their surroundings, which we call VizWiz-SalientObject. Compared to seven existing datasets, VizWiz-SalientObject is the largest (i.e., 32,000 human-annotated images) and contains unique characteristics including a higher prevalence of text in the salient objects (i.e., in 68\% of images) and salient objects that occupy a larger ratio of the images (i.e., on average, $\sim$50\% coverage). We benchmarked seven modern salient object detection methods on our dataset and found they struggle most with images featuring salient objects that are large, have less complex boundaries, and lack text as well as for lower quality images. We invite the broader community to work on our new dataset challenge by publicly sharing the dataset at https://vizwiz.org/tasks-and-datasets/salient-object .Comment: Computer Vision and Pattern Recognitio

RGB-D Salient Object Detection: A Survey

Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer vision tasks. Now, with the advent of depth sensors, depth maps with affluent spatial information that can be beneficial in boosting the performance of SOD, can easily be captured. Although various RGB-D based SOD models with promising performance have been proposed over the past several years, an in-depth understanding of these models and challenges in this topic remains lacking. In this paper, we provide a comprehensive survey of RGB-D based SOD models from various perspectives, and review related benchmark datasets in detail. Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well. Moreover, to investigate the SOD ability of existing models, we carry out a comprehensive evaluation, as well as attribute-based evaluation of several representative RGB-D based SOD models. Finally, we discuss several challenges and open directions of RGB-D based SOD for future research. All collected models, benchmark datasets, source code links, datasets constructed for attribute-based evaluation, and codes for evaluation will be made publicly available at https://github.com/taozh2017/RGBDSODsurveyComment: 24 pages, 12 figures. Has been accepted by Computational Visual Medi

注目領域検出のための視覚的注意モデル設計に関する研究

Visual attention is an important mechanism in the human visual system. When human observe images and videos, they usually do not describe all the contents in them. Instead, they tend to talk about the semantically important regions and objects in the images. The human eye is usually attracted by some regions of interest rather than the entire scene. These regions of interest that present the mainly meaningful or semantic content are called saliency region. Visual saliency detection refers to the use of intelligent algorithms to simulate human visual attention mechanism, extract both the low-level features and high-level semantic information and localize the salient object regions in images and videos. The generated saliency map indicates the regions that are likely to attract human attention. As a fundamental problem of image processing and computer vision, visual saliency detection algorithms have been extensively studied by researchers to solve practical tasks, such as image and video compression, image retargeting, object detection, etc. The visual attention mechanism adopted by saliency detection in general are divided into two categories, namely the bottom-up model and top-down model. The bottom-up attention algorithm focuses on utilizing the low-level visual features such as colour and edges to locate the salient objects. While the top-down attention utilizes the supervised learning to detect saliency. In recent years, more and more research tend to design deep neural networks with attention mechanisms to improve the accuracy of saliency detection. The design of deep attention neural network is inspired by human visual attention. The main goal is to enable the network to automatically capture the information that is critical to the target tasks and suppress irrelevant information, shift the attention from focusing on all to local. Currently various domain’s attention has been developed for saliency detection and semantic segmentation, such as the spatial attention module in convolution network, it generates a spatial attention map by utilizing the inter-spatial relationship of features; the channel attention module produces a attention by exploring the inter-channel relationship of features. All these well-designed attentions have been proven to be effective in improving the accuracy of saliency detection. This paper investigates the visual attention mechanism of salient object detection and applies it to digital histopathology image analysis for the detection and classification of breast cancer metastases. As shown in following contents, the main research contents include three parts: First, we studied the semantic attention mechanism and proposed a semantic attention approach to accurately localize the salient objects in complex scenarios. The proposed semantic attention uses Faster-RCNN to capture high-level deep features and replaces the last layer of Faster-RCNN by a FC layer and sigmoid function for visual saliency detection; it calculates proposals' attention probabilities by comparing their feature distances with the possible salient object. The proposed method introduces a re-weighting mechanism to reduce the influence of the complexity background, and a proposal selection mechanism to remove the background noise to obtain objects with accurate shape and contour. The simulation result shows that the semantic attention mechanism is robust to images with complex background due to the consideration of high-level object concept, the algorithm achieved outstanding performance among the salient object detection algorithms in the same period. Second, we designed a deep segmentation network (DSNet) for saliency object prediction. We explored a Pyramidal Attentional ASPP (PA-ASPP) module which can provide pixel level attention. DSNet extracts multi-level features with dilated ResNet-101 and the multiscale contextual information was locally weighted with the proposed PA-ASPP. The pyramid feature aggregation encodes the multi-level features from three different scales. This feature fusion incorporates neighboring scales of context features more precisely to produce better pixel-level attention. Finally, we use a scale-aware selection (SAS) module to locally weight multi-scale contextual features, capture important contexts of ASPP for the accurate and consistent dense prediction. The simulation results demonstrated that the proposed PA-ASPP is effective and can generate more coherent results. Besides, with the SAS, the model can adaptively capture the regions with different scales effectively. Finally, based on previous research on attentional mechanisms, we proposed a novel Deep Regional Metastases Segmentation (DRMS) framework for the detection and classification of breast cancer metastases. As we know, the digitalized whole slide image has high-resolution, usually has gigapixel, however the size of abnormal region is often relatively small, and most of the slide region are normal. The highly trained pathologists usually localize the regions of interest first in the whole slide, then perform precise examination in the selected regions. Even though the process is time-consuming and prone to miss diagnosis. Through observation and analysis, we believe that visual attention should be perfectly suited for the application of digital pathology image analysis. The integrated framework for WSI analysis can capture the granularity and variability of WSI, rich information from multi-grained pathological image. We first utilize the proposed attention mechanism based DSNet to detect the regional metastases in patch-level. Then, adopt the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to predict the whole metastases from individual slides. Finally, determine patient-level pN-stages by aggregating each individual slide-level prediction. In combination with the above techniques, the framework can make better use of the multi-grained information in histological lymph node section of whole-slice images. Experiments on large-scale clinical datasets (e.g., CAMELYON17) demonstrate that our method delivers advanced performance and provides consistent and accurate metastasis detection

Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The complex features of pixels in the lesion region impede the lesion segmentation accuracy, and existing deep learning-based methods often lack interpretability to this problem. In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED. Specifically, skin lesions are segmented by minimizing the structural entropy of a superpixel graph constructed from the dermoscopic image. Then, we characterize the consistency of healthy skin features and devise a novel multi-scale segmentation mechanism by outlier detection, which enhances the segmentation accuracy by leveraging the superpixel features from multiple scales. We conduct experiments on four skin lesion benchmarks and compare SLED with nine representative unsupervised segmentation methods. Experimental results demonstrate the superiority of the proposed framework. Additionally, some case studies are analyzed to demonstrate the effectiveness of SLED.Comment: 10 pages, 8 figures, conference. Accepted by IEEE ICDM 202