Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning

In many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. This method is built on parallel corpora produced by machine translation. It jointly minimizes the training error of each classifier in each language while penalizing the distance between the subspace representations of parallel documents. Our empirical study on a large set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Progressive Ensemble Networks for Zero-Shot Recognition

Despite the advancement of supervised image recognition algorithms, their dependence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to reduce human labeling effort. In this paper, we propose a novel progressive ensemble network model with multiple projected label embeddings to address zero-shot image recognition. The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes. A progressive training framework is then deployed to gradually label the most confident images in each unlabeled class with predicted pseudo-labels and update the ensemble network with the training data augmented by the pseudo-labels. The proposed model performs training on both labeled and unlabeled data. It can naturally bridge the domain shift problem in visual appearances and be extended to the generalized zero-shot learning scenario. We conduct experiments on multiple ZSL datasets and the empirical results demonstrate the efficacy of the proposed model.Comment: CVPR1

Exemplar Learning for Medical Image Segmentation

Medical image annotation typically requires expert knowledge and hence incurs time-consuming and expensive data annotation costs. To reduce this burden, we propose a novel learning scenario, Exemplar Learning (EL), to explore automated learning processes for medical image segmentation from a single annotated image example. This innovative learning task is particularly suitable for medical image segmentation, where all categories of organs can be presented in one single image for annotation all at once. To address this challenging EL task, we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework for medical image segmentation that enables innovative exemplar-based data synthesis, pixel-prototype based contrastive embedding learning, and pseudo-label based exploitation of the unlabeled data. Specifically, ELSNet introduces two new modules for image segmentation: an exemplar-guided synthesis module, which enriches and diversifies the training set by synthesizing annotated samples from the given exemplar, and a pixel-prototype based contrastive embedding module, which enhances the discriminative capacity of the base segmentation model via contrastive self-supervised learning. Moreover, we deploy a two-stage process for segmentation model training, which exploits the unlabeled data with predicted pseudo segmentation labels. To evaluate this new learning framework, we conduct extensive experiments on several organ segmentation datasets and present an in-depth analysis. The empirical results show that the proposed exemplar learning framework produces effective segmentation results