Multi-task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape

Solid Freeform Fabrication of Artificial Human Teeth

In this paper, we describe a solid freeform fabrication procedure for human dental restoration via porcelain slurry micro-extrusion. Based on submicron-sized dental porcelain powder obtained via ball milling process, a porcelain slurry formulation has been developed. The formulation developed allows the porcelain slurry to show a pseudoplastic behavior and moderate viscosity, which permits the slurry to re-shape to form a near rectangular cross section. A well-controlled cross-section geometry of the extrudate is important for micro-extrusion to obtain uniform 2-D planes and for the addition of the sequential layers to form a 3-D object. Human teeth are restored by this method directly from CAD digital models. After sintering, shrinkage of the artificial teeth is uniform in all directions. Microstructure of the sintered teeth is identical to that made via traditional dental restoration processes.Mechanical Engineerin

Laser Densification of Extruded Dental Porcelain Bodies in Multi-Material Laser Densification (MMLD) Process

In this study commercial dental porcelain powder was deposited via slurry extrusion and laser densified to fabricate dental restorations in a Multi-Material Laser Densification (MMLD) process. The processing conditions for laser densification of single lines and closed rings were investigated in order to avoid warping and cracking. Multi-layer rings were also investigated to study the dependence of bonding between layers on the laser densification conditions. The laser densified rings showed no warping, and good bonding between layers could be achieved when the laser densification condition was selected properly. The mechanism to achieve porcelain rings without warping and cracking is discussed. The understanding developed will pave the way for fabricating a physical dental restoration unit.Mechanical Engineerin

Bilingual sentence alignment of pre-Qin history literature for digital humanities study

Sentence aligned bilingual text of history literature provides support of digital resources for related digital humanities studies, but existing studies have done little work on sentence alignment of ancient Chinese and English. In this study, we made a preliminary attempt to align the sentence of ancient Chinese and English. We used the bilingual text of the Analects of Confucius and Zuo's Commentaries of the Spring and Autumn Annals, extracted features and adopted the classification method to divide the bilingual candidate sentence pairs based on probability scores. The bilingual sentence alignment model based on SVM had the best performance on a larger amount of data when using three features and confirmed the impact of candidate dataset

GAN Prior based Null-Space Learning for Consistent Super-Resolution

Consistency and realness have always been the two critical issues of image super-resolution. While the realness has been dramatically improved with the use of GAN prior, the state-of-the-art methods still suffer inconsistencies in local structures and colors (e.g., tooth and eyes). In this paper, we show that these inconsistencies can be analytically eliminated by learning only the null-space component while fixing the range-space part. Further, we design a pooling-based decomposition (PD), a universal range-null space decomposition for super-resolution tasks, which is concise, fast, and parameter-free. PD can be easily applied to state-of-the-art GAN Prior based SR methods to eliminate their inconsistencies, neither compromising the realness nor bringing extra parameters or computational costs. Besides, our ablation studies reveal that PD can replace pixel-wise losses for training and achieve better generalization performance when facing unseen downsamplings or even real-world degradation. Experiments show that the use of PD refreshes state-of-the-art SR performance and speeds up the convergence of training up to 2~10 times.Comment: Accepted by AAAI 202

Introspective Deep Metric Learning for Image Retrieval

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident semantic distances between images regardless of the uncertainty level. However, we argue that a good similarity model should consider the semantic discrepancies with caution to better deal with ambiguous images for more robust training. To achieve this, we propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. We further propose an introspective similarity metric to make similarity judgments between images considering both their semantic differences and ambiguities. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling and attains state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets for image retrieval and clustering. We further provide an in-depth analysis of our framework to demonstrate the effectiveness and reliability of IDML. Code is available at: https://github.com/wzzheng/IDML.Comment: The extended version of this paper is accepted to T-PAMI. Source code available at https://github.com/wzzheng/IDM