Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels

Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed. Unfortunately, this assumption is unrealistic in large-scale MLC data that has long-tailed (LT) distribution and partial labels (PL). To address the problem, we introduce a novel task, Partial labeling and Long-Tailed Multi-Label Classification (PLT-MLC), to jointly consider the above two imperfect learning environments. Not surprisingly, we find that most LT-MLC and PL-MLC approaches fail to solve the PLT-MLC, resulting in significant performance degradation on the two proposed PLT-MLC benchmarks. Therefore, we propose an end-to-end learning framework: \textbf{CO}rrection $\rightarrow$ \textbf{M}odificat\textbf{I}on $\rightarrow$ balan\textbf{C}e, abbreviated as \textbf{\method{}}. Our bootstrapping philosophy is to simultaneously correct the missing labels (Correction) with convinced prediction confidence over a class-aware threshold and to learn from these recall labels during training. We next propose a novel multi-focal modifier loss that simultaneously addresses head-tail imbalance and positive-negative imbalance to adaptively modify the attention to different samples (Modification) under the LT class distribution. In addition, we develop a balanced training strategy by distilling the model's learning effect from head and tail samples, and thus design a balanced classifier (Balance) conditioned on the head and tail learning effect to maintain stable performance for all samples. Our experimental study shows that the proposed \method{} significantly outperforms general MLC, LT-MLC and PL-MLC methods in terms of effectiveness and robustness on our newly created PLT-MLC datasets

Controllable Multi-Objective Re-ranking with Policy Hypernetworks

Multi-stage ranking pipelines have become widely used strategies in modern recommender systems, where the final stage aims to return a ranked list of items that balances a number of requirements such as user preference, diversity, novelty etc. Linear scalarization is arguably the most widely used technique to merge multiple requirements into one optimization objective, by summing up the requirements with certain preference weights. Existing final-stage ranking methods often adopt a static model where the preference weights are determined during offline training and kept unchanged during online serving. Whenever a modification of the preference weights is needed, the model has to be re-trained, which is time and resources inefficient. Meanwhile, the most appropriate weights may vary greatly for different groups of targeting users or at different time periods (e.g., during holiday promotions). In this paper, we propose a framework called controllable multi-objective re-ranking (CMR) which incorporates a hypernetwork to generate parameters for a re-ranking model according to different preference weights. In this way, CMR is enabled to adapt the preference weights according to the environment changes in an online manner, without retraining the models. Moreover, we classify practical business-oriented tasks into four main categories and seamlessly incorporate them in a new proposed re-ranking model based on an Actor-Evaluator framework, which serves as a reliable real-world testbed for CMR. Offline experiments based on the dataset collected from Taobao App showed that CMR improved several popular re-ranking models by using them as underlying models. Online A/B tests also demonstrated the effectiveness and trustworthiness of CMR

A Dual-Platform Laser Scanner for 3D Reconstruction of Dental Pieces

This paper presents a dual-platform scanner for dental reconstruction based on a three-dimensional (3D) laser-scanning method. The scanner combines translation and rotation platforms to perform a holistic scanning. A hybrid calibration method for laser scanning is proposed to improve convenience and precision. This method includes an integrative method for data collection and a hybrid algorithm for data processing. The integrative method conveniently collects a substantial number of calibrating points with a stepped gauge and a pattern for both the translation and rotation scans. The hybrid algorithm, which consists of a basic model and a compensation network, achieves strong stability with a small degree of errors. The experiments verified the hybrid calibration method and the scanner application for the measurement of dental pieces. Two typical dental pieces were measured, and the experimental results demonstrated the validity of the measurement that was performed using the dual-platform scanner. This method is effective for the 3D reconstruction of dental pieces, as well as that of objects with irregular shapes in engineering fields. Keywords: Laser scanning, Hybrid calibration, Neural network, Dental piece

MagicMirror [smart clothing modelling mirror]

Mirrors are usually placed inside washroom, near closets, or where it is convenient for people to glance at after changing or tidying themselves up.  Have you ever had the trouble of losing track of your time while you are changing? Have you ever worn the wrong clothes because you are not sure of what the weather is like outside? Have you ever felt frustrated about taking a selfie using your cell phone after you change because the phone keeps blocking part of your outfit? Well, these are the reasons why our company ShowMi Technology built a smart mirror called ShowMi which can solve all these difficulties. The main purpose of ShowMi is to provide a function like any other mirrors in the market whilst adding extraordinary, convenient, and interesting features. This mirror will be designed to display information such as time, and weather on the surface of the mirror. At the same time, we will place a built-in camera inside the mirror such that you can take a selfie with your new appearance with just the press of a button. We will also design an app that supports both Android and IOS that would allow you to select different interesting backgrounds on your cellphone and after you take a picture, you can be wearing your new clothes standing in Wall Street or even next to President Obama. To make everything even simpler, after the photos are taken, they will be sent directly to your cellphone for further photoshopping if you think it is necessary