A Superimposed Divide-and-Conquer Image Recognition Method for SEM Images of Nanoparticles on The Surface of Monocrystalline silicon with High Aggregation Degree
North China University of Science and Technology（华北理工大学）
The nanoparticle size and distribution information in the SEM images of silicon crystals are generally counted by manual methods. The realization of automatic machine recognition is significant in materials science. This paper proposed a superposition partitioning image recognition method to realize automatic recognition and information statistics of silicon crystal nanoparticle SEM images. Especially for the complex and highly aggregated characteristics of silicon crystal particle size, an accurate recognition step and contour statistics method based on morphological processing are given. This method has technical reference value for the recognition of Monocrystalline silicon surface nanoparticle images under different SEM shooting conditions. Besides, it outperforms other methods in terms of recognition accuracy and algorithm efficiency.
Huazhong University of Science and Technology（华中科技大学）；Research Institute of Huazhong University of Science and Technology in Shenzhen（华中科技大学深圳研究院）；Deeproute Co. Ltd（元戎启行有限公司）；Southern University of Science and Technology（南方科技大学）；Guizhou Normal University（贵州师范大学）
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed. Despite their empirical success on some scenarios, existing cross-modal hashing methods usually fail to cross modality gap when fully-paired data with plenty of labeled information is nonexistent. To circumvent this drawback, motivated by the Divide-and-Conquer strategy, we propose Deep Manifold Hashing (DMH), a novel method of dividing the problem of semi-paired unsupervised cross-modal retrieval into three sub-problems and building one simpl…
The Chinese University of Hong Kong（香港中文大学）；University of Oxford（牛津大学）；The University of Hong Kong（香港大学）；SmartMore（思谋科技）
Adversarial training is promising for improving robustness of deep neural networks towards adversarial perturbations, especially on the classification task. The effect of this type of training on semantic segmentation, contrarily, just commences. We make the initial attempt to explore the defense strategy on semantic segmentation by formulating a general adversarial training procedure that can perform decently on both adversarial and clean samples. We propose a dynamic divide-and-conquer adversarial training (DDC-AT) strategy to enhance the defense effect, by setting additional branches in the…
Semantics versus Identity: A Divide-and-Conquer Approach towards Adjustable Medical Image De-Identification
Shanghai AI Laboratory（上海人工智能实验室）；Institute of Image Communication and Network Engineering, Shanghai Jiao Tong Unversity（图像通信与网络工程学院，上海交通大学）；Cooperative Medianet Innovation Center, Shanghai Jiao Tong Unversity（合作中网创新中心，上海交通大学）
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
Academy for Engineering and Technology, Fudan University（复旦大学工程技术学院）；Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东计算中心（国家济南超级计算中心））
Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their …
Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications. Existing approaches usually learn a single metric in the embedding space for all available data points, which may have a very complex non-uniform distribution with different notions of similarity between objects, e.g. appearance, shape, color or semantic meaning. Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well. In this work, we…
Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided groundtruth training labels, a lot of additional vis…
Image segmentation needs both local boundary position information and global object context information. The performance of the recent state-of-the-art method, fully convolutional networks, reaches a bottleneck due to the neural network limit after balancing between the two types of information simultaneously in an end-to-end training style. To overcome this problem, we divide the semantic image segmentation into temporal subtasks. First, we find a possible pixel position of some object boundary; then trace the boundary at steps within a limited length until the whole object is outlined. We pr…
Jolia: Concept-Level Vision-Language Alignment for 3D CT Contrastive Learning
Raidium；Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, AP-HP（欧洲乔治·蓬皮杜医院血管与肿瘤介入放射科，AP-HP）；Faculté de Santé, Université Paris-Cité（巴黎西岱大学健康学院）；HEKA, INRIA（HEKA，法国国家信息与自动化研究所）；Imaging Department, Fondation Ophtalmologique Adolphe de Rothschild（阿道夫·罗斯柴尔德眼科基金会影像科）
Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation
Nanjing University of Posts and Telecommunications（南京邮电大学）；Surrey Institute for People-Centred Artificial Intelligence（萨里以人为本人工智能研究所）；University of Surrey（萨里大学）
