OpenRaiser/Intern-Atlas huggingface dataset:
Deep Multi Label Classification in Affine Subspaces
Venue: arXiv cs.CV 2019
Year: 2019

Multi-label classification (MLC) problems are becoming increasingly popular in the context of medical imaging. This has in part been driven by the fact that acquiring annotations for MLC is far less burdensome than for semantic segmentation and yet provides more expressiveness than multi-class classification. However, to train MLCs, most methods have resorted to similar objective functions as with traditional multi-class classification settings. We show in this work that such approaches are not optimal and instead propose a novel deep MLC classification method in affine subspace. At its core, the method attempts to pull features of class-labels towards different affine subspaces while maximizing the distance between them. We evaluate the method using two MLC medical imaging datasets and show a large performance increase compared to previous multi-label frameworks. This method can be seen as a plug-in replacement loss function and is trainable in an end-to-end fashion.
Progressive Wasserstein Barycenters of Persistence Diagrams
Venue: arXiv cs.GR 2019
Year: 2019

This paper presents an efficient algorithm for the progressive approximation of Wasserstein barycenters of persistence diagrams, with applications to the visual analysis of ensemble data. Given a set of scalar fields, our approach enables the computation of a persistence diagram which is representative of the set, and which visually conveys the number, data ranges and saliences of the main features of interest found in the set. Such representative diagrams are obtained by computing explicitly the discrete Wasserstein barycenter of the set of persistence diagrams, a notoriously computationally intensive task. In particular, we revisit efficient algorithms for Wasserstein distance approximation [12,51] to extend previous work on barycenter estimation [94]. We present a new fast algorithm, which progressively approximates the barycenter by iteratively increasing the computation accuracy as well as the number of persistent features in the output diagram. Such a progressivity drastically improves convergence in practice and allows to design an interruptible algorithm, capable of respecting computation time constraints. This enables the approximation of Wasserstein barycenters within interactive times. We present an application to ensemble clustering where we revisit the k-means algorithm to exploit our barycenters and compute, within execution time constraints, meaningful clusters of ensemble data along with their barycenter diagram. Extensive experiments on synthetic and real-life data sets report that our algorithm converges to barycenters that are qualitatively meaningful with regard to the applications, and quantitatively comparable to previous techniques, while offering an order of magnitude speedup when run until convergence (without time constraint). Our algorithm can be trivially parallelized to provide additional speedups in practice on standard workstations. [...]
Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation
Venue: arXiv cs.RO 2019
Year: 2019

Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving. However, producing the vast quantities of road marking labels required for training state-of-the-art deep networks is costly, time-consuming, and simply infeasible for every domain and condition. In addition, training data retrieved from virtual worlds often lack the richness and complexity of the real world and consequently cannot be used directly. In this paper, we provide an alternative approach in which new road marking training pairs are automatically generated. To this end, we apply principles of domain randomization to the road layout and synthesize new images from altered semantic labels. We demonstrate that training on these synthetic pairs improves mIoU of the segmentation of rare road marking classes during real-world deployment in complex urban environments by more than 12 percentage points, while performance for other classes is retained. This framework can easily be scaled to all domains and conditions to generate large-scale road marking datasets, while avoiding manual labelling effort.
Dunhuang Grottoes Painting Dataset and Benchmark
Venue: arXiv cs.CV 2019
Year: 2019

This document introduces the background and the usage of the Dunhuang Grottoes Dataset and the benchmark. The documentation first starts with the background of the Dunhuang Grotto, which is widely recognised as an priceless heritage. Given that digital method is the modern trend for heritage protection and restoration. Follow the trend, we release the first public dataset for Dunhuang Grotto Painting restoration. The rest of the documentation details the painting data generation. To enable a data driven fashion, this dataset provided a large number of training and testing example which is sufficient for a deep learning approach. The detailed usage of the dataset as well as the benchmark is described.
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects
Venue: arXiv cs.IR 2019
Year: 2019

Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems. The time required to present query results to a user is paramount to the users satisfaction. Recent advances in neural IR re-ranking models put the issue of query latency at the forefront. They bring a complex trade-off between performance and effectiveness based on a myriad of factors: the choice of encoding model, network architecture, hardware acceleration and many others. The best performing models (currently using the BERT transformer model) run orders of magnitude more slowly than simpler architectures. We aim to broaden the focus of the neural IR community to include performance considerations -- to sustain the practical applicability of our innovations. In this position paper we supply our argument with a case study exploring the performance of different neural re-ranking models. Finally, we propose to extend the OSIRRC docker-based replicability infrastructure with two performance focused benchmark scenarios.
Lingua Custodia at WMT'19: Attempts to Control Terminology
Venue: arXiv cs.CL 2019
Year: 2019

This paper describes Lingua Custodia's submission to the WMT'19 news shared task for German-to-French on the topic of the EU elections. We report experiments on the adaptation of the terminology of a machine translation system to a specific topic, aimed at providing more accurate translations of specific entities like political parties and person names, given that the shared task provided no in-domain training parallel data dealing with the restricted topic. Our primary submission to the shared task uses backtranslation generated with a type of decoding allowing the insertion of constraints in the output in order to guarantee the correct translation of specific terms that are not necessarily observed in the data.
Minimal linear codes arising from blocking sets
Venue: arXiv cs.IT 2019
Year: 2019

Minimal linear codes are algebraic objects which gained interest in the last twenty years, due to their link with Massey's secret sharing schemes. In this context, Ashikhmin and Barg provided a useful and a quite easy to handle sufficient condition for a linear code to be minimal, which has been applied in the construction of many minimal linear codes. In this paper, we generalize some recent constructions of minimal linear codes which are not based on Ashikhmin-Barg's condition. More combinatorial and geometric methods are involved in our proofs. In particular, we present a family of codes arising from particular blocking sets, which are well-studied combinatorial objects. In this context, we will need to define cutting blocking sets and to prove some of their relations with other notions in blocking sets' theory. At the end of the paper, we provide one explicit family of cutting blocking sets and related minimal linear codes, showing that infinitely many of its members do not satisfy the Ashikhmin-Barg's condition.
EPNAS: Efficient Progressive Neural Architecture Search
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{Williams.1992.PG}. EPNAS is designed to search target networks in parallel, which is more scalable on parallel systems such as GPU/TPU clusters. More importantly, EPNAS can be generalized to architecture search with multiple resource constraints, \eg, model size, compute complexity or intensity, which is crucial for deployment in widespread platforms such as mobile and cloud. We compare EPNAS against other state-of-the-art (SoTA) network architectures (\eg, MobileNetV2~\cite{mobilenetv2}) and efficient NAS algorithms (\eg, ENAS~\cite{pham2018efficient}, and PNAS~\cite{Liu2017b}) on image recognition tasks using CIFAR10 and ImageNet. On both datasets, EPNAS is superior \wrt architecture searching speed and recognition accuracy.
A Theoretical Model For Artificial Learning, Memory Management And Decision Making System
Venue: arXiv cs.OH 2019
Year: 2019

Human beings are considered as the most intelligent species on Earth. The ability to think, to create, to innovate, are the key elements which make humans superior over other existing species on Earth. Machines lack all those elements, although machines are faster than human in aspects like computing, equating etc. But humans are still more valuable than machines, due to all those previously discussed elements. Various models have been developed in last few years to create models that can think like human beings, but are not completely successful. This paper presents a new theoretical system for learning, memory management and decision making that can be used to develop highly complex systems, and shows the potential to be used for development of systems that can be used to provide the essential features to the machines to act like human beings.
Lexicase selection in Learning Classifier Systems
Venue: arXiv cs.NE 2019
Year: 2019

The lexicase parent selection method selects parents by considering performance on individual data points in random order instead of using a fitness function based on an aggregated data accuracy. While the method has demonstrated promise in genetic programming and more recently in genetic algorithms, its applications in other forms of evolutionary machine learning have not been explored. In this paper, we investigate the use of lexicase parent selection in Learning Classifier Systems (LCS) and study its effect on classification problems in a supervised setting. We further introduce a new variant of lexicase selection, called batch-lexicase selection, which allows for the tuning of selection pressure. We compare the two lexicase selection methods with tournament and fitness proportionate selection methods on binary classification problems. We show that batch-lexicase selection results in the creation of more generic rules which is favorable for generalization on future data. We further show that batch-lexicase selection results in better generalization in situations of partial or missing data.
ReQA: An Evaluation for End-to-End Answer Retrieval Models
Venue: arXiv cs.CL 2019
Year: 2019

Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question-Answering (ReQA), a benchmark for evaluating large-scale sentence-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.
AWG-based Nonblocking Shuffle-Exchange Networks
Venue: arXiv cs.NI 2019
Year: 2019

Optical shuffle-exchange networks (SENs) have wide application in different kinds of interconnection networks. This paper proposes an approach to construct modular optical SENs, using a set of arrayed waveguide gratings (AWGs) and tunable wavelength converters (TWCs). According to the wavelength routing property of AWGs, we demonstrate for the first time that an AWG is functionally equivalent to a classical shuffle network by nature. Based on this result, we devise a systematic method to design a large-scale wavelength-division-multiplexing (WDM) shuffle network using a set of small-size AWGs associated with the same wavelength set. Combining the AWG-based WDM shuffle networks and the TWCs with small conversion range, we finally obtain an AWG-based WDM SEN, which not only is scalable in several ways, but also can achieve 100% utilization when the input wavelength channels are all busy. We also study the routing and wavelength assignment (RWA) problem of the AWG-based WDM SEN, and prove that the self-routing property and the nonblocking routing conditions of classical SENs are preserved in such AWG-based WDM SEN.
CallE: An Effect System for Method Calls
Venue: arXiv cs.PL 2019
Year: 2019

Effect systems are used to statically reason about the effects an expression may have when evaluated. In the literature, such effects include various behaviours as diverse as memory accesses and exception throwing. Here we present CallE, an object-oriented language that takes a flexible approach where effects are just method calls: this works well because ordinary methods often model things like I/O operations, access to global state, or primitive language operations such as thread creation. CallE supports both flexible and fine-grained control over such behaviour, in a way designed to minimise the complexity of annotations. CallE's effect system can be used to prevent OO code from performing privileged operations, such as querying a database, modifying GUI widgets, exiting the program, or performing network communication. It can also be used to ensure determinism, by preventing methods from (indirectly) calling non-deterministic primitives like random number generation or file reading.
Agile Domain Adaptation
Venue: arXiv cs.CV 2019
Year: 2019

Domain adaptation investigates the problem of leveraging knowledge from a well-labeled source domain to an unlabeled target domain, where the two domains are drawn from different data distributions. Because of the distribution shifts, different target samples have distinct degrees of difficulty in adaptation. However, existing domain adaptation approaches overwhelmingly neglect the degrees of difficulty and deploy exactly the same framework for all of the target samples. Generally, a simple or shadow framework is fast but rough. A sophisticated or deep framework, on the contrary, is accurate but slow. In this paper, we aim to challenge the fundamental contradiction between the accuracy and speed in domain adaptation tasks. We propose a novel approach, named {\it agile domain adaptation}, which agilely applies optimal frameworks to different target samples and classifies the target samples according to their adaptation difficulties. Specifically, we propose a paradigm which performs several early detections before the final classification. If a sample can be classified at one of the early stage with enough confidence, the sample would exit without the subsequent processes. Notably, the proposed method can significantly reduce the running cost of domain adaptation approaches, which can extend the application scenarios of domain adaptation to even mobile devices and real-time systems. Extensive experiments on two open benchmarks verify the effectiveness and efficiency of the proposed method.
Aesthetic Attributes Assessment of Images
Venue: arXiv cs.CV 2019
Year: 2019

Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named \emph{DPC-Captions} which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions.
Semi-supervised Feature-Level Attribute Manipulation for Fashion Image Retrieval
Venue: arXiv cs.CV 2019
Year: 2019

With a growing demand for the search by image, many works have studied the task of fashion instance-level image retrieval (FIR). Furthermore, the recent works introduce a concept of fashion attribute manipulation (FAM) which manipulates a specific attribute (e.g color) of a fashion item while maintaining the rest of the attributes (e.g shape, and pattern). In this way, users can search not only "the same" items but also "similar" items with the desired attributes. FAM is a challenging task in that the attributes are hard to define, and the unique characteristics of a query are hard to be preserved. Although both FIR and FAM are important in real-life applications, most of the previous studies have focused on only one of these problem. In this study, we aim to achieve competitive performance on both FIR and FAM. To do so, we propose a novel method that converts a query into a representation with the desired attributes. We introduce a new idea of attribute manipulation at the feature level, by matching the distribution of manipulated features with real features. In this fashion, the attribute manipulation can be done independently from learning a representation from the image. By introducing the feature-level attribute manipulation, the previous methods for FIR can perform attribute manipulation without sacrificing their retrieval performance.
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
Venue: arXiv cs.CV 2019
Year: 2019

We present BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference. It runs at a speed of 200-1000+ FPS on flagship devices. This super-realtime performance enables it to be applied to any augmented reality pipeline that requires an accurate facial region of interest as an input for task-specific models, such as 2D/3D facial keypoint or geometry estimation, facial features or expression classification, and face region segmentation. Our contributions include a lightweight feature extraction network inspired by, but distinct from MobileNetV1/V2, a GPU-friendly anchor scheme modified from Single Shot MultiBox Detector (SSD), and an improved tie resolution strategy alternative to non-maximum suppression.
Safe Policy Improvement with Soft Baseline Bootstrapping
Venue: arXiv cs.LG 2019
Year: 2019

Batch Reinforcement Learning (Batch RL) consists in training a policy using trajectories collected with another policy, called the behavioural policy. Safe policy improvement (SPI) provides guarantees with high probability that the trained policy performs better than the behavioural policy, also called baseline in this setting. Previous work shows that the SPI objective improves mean performance as compared to using the basic RL objective, which boils down to solving the MDP with maximum likelihood. Here, we build on that work and improve more precisely the SPI with Baseline Bootstrapping algorithm (SPIBB) by allowing the policy search over a wider set of policies. Instead of binarily classifying the state-action pairs into two sets (the \textit{uncertain} and the \textit{safe-to-train-on} ones), we adopt a softer strategy that controls the error in the value estimates by constraining the policy change according to the local model uncertainty. The method can take more risks on uncertain actions all the while remaining provably-safe, and is therefore less conservative than the state-of-the-art methods. We propose two algorithms (one optimal and one approximate) to solve this constrained optimization problem and empirically show a significant improvement over existing SPI algorithms both on finite MDPs and on infinite MDPs with a neural network function approximation.
MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment
Venue: arXiv cs.CL 2019
Year: 2019

Building computer systems that can converse about their visual environment is one of the oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for example, Winograd's 1972 SHRDLU system). Only recently, however, have methods from computer vision and natural language processing become powerful enough to make this vision seem more attainable. Pushed especially by developments in computer vision, many data sets and collection environments have recently been published that bring together verbal interaction and visual processing. Here, we argue that these datasets tend to oversimplify the dialogue part, and we propose a task---MeetUp!---that requires both visual and conversational grounding, and that makes stronger demands on representations of the discourse. MeetUp! is a two-player coordination game where players move in a visual environment, with the objective of finding each other. To do so, they must talk about what they see, and achieve mutual understanding. We describe a data collection and show that the resulting dialogues indeed exhibit the dialogue phenomena of interest, while also challenging the language & vision aspect.
Efficient Semantic Scene Completion Network with Spatial Group Convolution
Venue: arXiv cs.CV 2019
Year: 2019

We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimensions rather than feature channel dimension. It divides input voxels into different groups, then conducts 3D sparse convolution on these separated groups. As only valid voxels are considered when performing convolution, computation can be significantly reduced with a slight loss of accuracy. The proposed operations are validated on semantic scene completion task, which aims to predict a complete 3D volume with semantic labels from a single depth image. With SGC, we further present an efficient 3D sparse convolutional network, which harnesses a multiscale architecture and a coarse-to-fine prediction strategy. Evaluations are conducted on the SUNCG dataset, achieving state-of-the-art performance and fast speed. Code is available at https://github.com/zjhthu/SGC-Release.git