Existing supervised quantization methods usually learn the quantizers from pair-wise, triplet, or anchor-based losses, which only capture their relationship locally without aligning them globally. This may cause an inadequate use of the entire space and a severe intersection among different semantics, leading to inferior retrieval performance. Furthermore, to enable quantizers to learn in an end-to-end way, current practices usually relax the non-differentiable quantization operation by substituting it with softmax, which unfortunately is biased, leading to an unsatisfying suboptimal solution. To address the above issues, we present Spherical Centralized Quantization (SCQ), which contains a Priori Knowledge based Feature (PKFA) module for the global alignment of feature vectors, and an Annealing Regulation Semantic Quantization (ARSQ) module for low-biased optimization. Specifically, the PKFA module first applies Semantic Center Allocation (SCA) to obtain semantic centers based on prior knowledge, and then adopts Centralized Feature Alignment (CFA) to gather feature vectors based on corresponding semantic centers. The SCA and CFA globally optimize the inter-class separability and intra-class compactness, respectively. After that, the ARSQ module performs a partial-soft relaxation to tackle biases, and an Annealing Regulation Quantization loss for further addressing the local optimal solution. Experimental results show that our SCQ outperforms state-of-the-art algorithms by a large margin (2.1%, 3.6%, 5.5% mAP respectively) on CIFAR-10, NUS-WIDE, and ImageNet with a code length of 8 bits. Codes are publicly available:https://github.com/zzb111/Spherical-Centralized-Quantization.

Manipulating visual attributes of an image through a natural language description, known as text-to-image attributes manipulation (T2AM), is a challenging task. However, existing approaches tend to search the whole image to manipulate the target instance indicated by a description, thus they often fail to locate and manipulate the accurate text-relevant regions, and even disturb the text-irrelevant contents, e.g. texture and background. Meanwhile, the model efficiency needs to be improved. To tackle the above issues, we introduce a novel yet simple GAN-based approach, namelyStructuringImage forManipulating(SIMGAN), to narrow down the optimization areas from external to internal. It consists of two major components: 1)External Structuring(ExST), a pretrained segmentation network, for recognizing and separating the target instances and background from an image; and 2)Internal Structuring(InST) for seeking out and editing the text-relevant attributes of the target instances based on the given description and masked hierarchical image representations from ExST. Specifically, the InST structures target instances from outline to detail by firstly drawing the sketch and colors underpainting of instances with anOutline-Oriented Structuring(OuST), and then enhancing the text-relevant attributes and elaborating on details with aDetail-Oriented Structuring(DeST). Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms state-of-the-art both quantitatively and qualitatively. Compared with the state-of-the-art method ManiGAN, our approach reduces the training time by 88%, while the inferring time is three times faster. In addition, our approach is easily extended to solve the instance-level image-to-image translation problem, and the results exhibit the versatility and effectiveness of our approach. This code is released inhttps://github.com/qikizh/SIMGAN.

Montreal Cognitive Assessment (MoCA) test is an auxiliary medical screening method for Alzheimer's disease (AD). During the traditional process, a testee is required to conduct several test items on the paper questionnaire following the guidance of a medical staff. It is inefficient and dependents largely on the doctor's subjective judgment and experience level. Therefore, we propose an Interactive and Intelligent AD Auxiliary Screening (IAS) system consisting of speech-based Interactive Unit Testing Module (IUTM) and truth-based Intelligent Analysis Module (IAM), both of which are developed by deep learning techniques. Following the guidance of voice commands, the testee could achieve the MoCA test independently in IUTM just by a mobile device, and then the testing data is analyzed accurately and objectively by IAM. Moreover, the electronic system is beneficial to collect and analyze clinical data for further research compared to the traditional method. And the system is deployed in the Department of Neurology, Sichuan Provincial People's Hospital in June 2021 and has been used in the clinical screening of Alzheimer's disease.