OpenRaiser/Intern-Atlas huggingface dataset:
Historical and Modern Features for Buddha Statue Classification
Venue: arXiv cs.CV 2019
Year: 2019

While Buddhism has spread along the Silk Roads, many pieces of art have been displaced. Only a few experts may identify these works, subjectively to their experience. The construction of Buddha statues was taught through the definition of canon rules, but the applications of those rules greatly varies across time and space. Automatic art analysis aims at supporting these challenges. We propose to automatically recover the proportions induced by the construction guidelines, in order to use them and compare between different deep learning features for several classification tasks, in a medium size but rich dataset of Buddha statues, collected with experts of Buddhism art history.
Toward Robust Image Classification
Venue: arXiv cs.CV 2019
Year: 2019

Neural networks are frequently used for image classification, but can be vulnerable to misclassification caused by adversarial images. Attempts to make neural network image classification more robust have included variations on preprocessing (cropping, applying noise, blurring), adversarial training, and dropout randomization. In this paper, we implemented a model for adversarial detection based on a combination of two of these techniques: dropout randomization with preprocessing applied to images within a given Bayesian uncertainty. We evaluated our model on the MNIST dataset, using adversarial images generated using Fast Gradient Sign Method (FGSM), Jacobian-based Saliency Map Attack (JSMA) and Basic Iterative Method (BIM) attacks. Our model achieved an average adversarial image detection accuracy of 97%, with an average image classification accuracy, after discarding images flagged as adversarial, of 99%. Our average detection accuracy exceeded that of recent papers using similar techniques.
Decomposing Textual Information For Style Transfer
Venue: arXiv cs.CL 2019
Year: 2019

This paper focuses on latent representations that could effectively decompose different aspects of textual information. Using a framework of style transfer for texts, we propose several empirical methods to assess information decomposition quality. We validate these methods with several state-of-the-art textual style transfer methods. Higher quality of information decomposition corresponds to higher performance in terms of bilingual evaluation understudy (BLEU) between output and human-written reformulations.
A weakly supervised adaptive triplet loss for deep metric learning
Venue: arXiv cs.CV 2019
Year: 2019

We address the problem of distance metric learning in visual similarity search, defined as learning an image embedding model which projects images into Euclidean space where semantically and visually similar images are closer and dissimilar images are further from one another. We present a weakly supervised adaptive triplet loss (ATL) capable of capturing fine-grained semantic similarity that encourages the learned image embedding models to generalize well on cross-domain data. The method uses weakly labeled product description data to implicitly determine fine grained semantic classes, avoiding the need to annotate large amounts of training data. We evaluate on the Amazon fashion retrieval benchmark and DeepFashion in-shop retrieval data. The method boosts the performance of triplet loss baseline by 10.6% on cross-domain data and out-performs the state-of-art model on all evaluation metrics.
EPOSIT: An Absolute Pose Estimation Method for Pinhole and Fish-Eye Cameras
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a generic 6DOF camera pose estimation method, which can be used for both the pinhole camera and the fish-eye camera. Different from existing methods, relative positions of 3D points rather than absolute coordinates in the world coordinate system are employed in our method, and it has a unique solution. The application scope of POSIT (Pose from Orthography and Scaling with Iteration) algorithm is generalized to fish-eye cameras by combining with the radially symmetric projection model. The image point relationship between the pinhole camera and the fish-eye camera is derived based on their projection model. The general pose expression which fits for different cameras can be acquired by four noncoplanar object points and their corresponding image points. Accurate estimation results are calculated iteratively. Experimental results on synthetic and real data show that the pose estimation results of our method are more stable and accurate than state-of-the-art methods. The source code is available at https://github.com/k032131/EPOSIT.
Learning Category Correlations for Multi-label Image Recognition with Graph Networks
Venue: arXiv cs.CV 2019
Year: 2019

Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies. Previous existing methods resort to either recurrent networks or pre-defined label correlation graphs for this purpose. In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies. Specifically, we introduce a plug-and-play Label Graph (LG) module to learn label correlations with word embeddings, and then utilize traditional GCN to map this graph into label-dependent object classifiers which are further applied to image features. The basic LG module incorporates two 1x1 convolutional layers and uses the dot product to generate label graphs. In addition, we propose a sparse correlation constraint to enhance the LG module and also explore different LG architectures. We validate our method on two diverse multi-label datasets: MS-COCO and Fashion550K. Experimental results show that our A-GCN significantly improves baseline methods and achieves performance superior or comparable to the state of the art.
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
Venue: arXiv cs.CE 2019
Year: 2019

Deformation microstructure heterogeneities play a pivotal role during dislocation patterning and interface network restructuring. Thus, they affect indirectly how an alloy recrystallizes if at all. Given this relevance, it has become common practice to study the evolution of deformation microstructure heterogeneities with 3D experiments and full-field crystal plasticity computer simulations including tools such as the spectral method. Quantifying material point to grain or phase boundary distances, though, is a practical challenge with spectral method crystal plasticity models because these discretize the material volume rather than mesh explicitly the grain and phase boundary interface network. This limitation calls for the development of interface reconstruction algorithms which enable us to develop specific data post-processing protocols to quantify spatial correlations between state variable values at each material point and the points' corresponding distance to the closest grain or phase boundary. This work contributes to advance such post-processing routines. Specifically, two grain reconstruction and three distancing methods are developed to solve above challenge. The individual strengths and limitations of these methods surplus the efficiency of their parallel implementation is assessed with an exemplary DAMASK large scale crystal plasticity study. We apply the new tool to assess the evolution of subtle stress and disorientation gradients towards grain boundaries.
Training convolutional neural networks with cheap convolutions and online distillation
Venue: arXiv cs.CV 2019
Year: 2019

The large memory and computation consumption in convolutional neural networks (CNNs) has been one of the main barriers for deploying them on resource-limited systems. To this end, most cheap convolutions (e.g., group convolution, depth-wise convolution, and shift convolution) have recently been used for memory and computation reduction but with the specific architecture designing. Furthermore, it results in a low discriminability of the compressed networks by directly replacing the standard convolution with these cheap ones. In this paper, we propose to use knowledge distillation to improve the performance of the compact student networks with cheap convolutions. In our case, the teacher is a network with the standard convolution, while the student is a simple transformation of the teacher architecture without complicated redesigning. In particular, we propose a novel online distillation method, which online constructs the teacher network without pre-training and conducts mutual learning between the teacher and student network, to improve the performance of the student model. Extensive experiments demonstrate that the proposed approach achieves superior performance to simultaneously reduce memory and computation overhead of cutting-edge CNNs on different datasets, including CIFAR-10/100 and ImageNet ILSVRC 2012, compared to the state-of-the-art CNN compression and acceleration methods. The codes are publicly available at https://github.com/EthanZhangYC/OD-cheap-convolution.
W-RNN: News text classification based on a Weighted RNN
Venue: arXiv cs.IR 2019
Year: 2019

Most of the information is stored as text, so text mining is regarded as having high commercial potential. Aiming at the semantic constraint problem of classification methods based on sparse representation, we propose a weighted recurrent neural network (W-RNN), which can fully extract text serialization semantic information. For the problem that the feature high dimensionality and unclear semantic relationship in text data representation, we first utilize the word vector to represent the vocabulary in the text and use Recurrent Neural Network (RNN) to extract features of the serialized text data. The word vector is then automatically weighted and summed using the intermediate output of the word vector to form the text representation vector. Finally, the neural network is used for classification. W-RNN is verified on the news dataset and proves that W-RNN is superior to other four baseline methods in Precision, Recall, F1 and loss values, which is suitable for text classification.
Facial Expression Recognition Using Disentangled Adversarial Learning
Venue: arXiv cs.CV 2019
Year: 2019

The representation used for Facial Expression Recognition (FER) usually contain expression information along with other variations such as identity and illumination. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) to explicitly disentangle facial expression representation from identity information. In this learning by reconstruction method, facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. This expression representation is disentangled from identity component by explicitly providing the identity code to the decoder part of DE-GAN. The process of expression image reconstruction and disentangled expression representation learning is improved by performing expression and identity classification in the discriminator of DE-GAN. The disentangled facial expression representation is then used for facial expression recognition employing simple classifiers like SVM or MLP. The experiments are performed on publicly available and widely used face expression databases (CK+, MMI, Oulu-CASIA). The experimental results show that the proposed technique produces comparable results with state-of-the-art methods.
Query-Specific Knowledge Summarization with Entity Evolutionary Networks
Venue: arXiv cs.IR 2019
Year: 2019

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge summarization. For example, given a query "computer vision" on a CS literature corpus, rather than returning a list of relevant entities like "cnn", "imagenet" and "svm", we are interested in the connections among them, and furthermore, the evolution patterns of such connections along particular ordinal dimensions such as time. Particularly, we hope to provide structural knowledge relevant to the query, such as "svm" is related to "imagenet" but not "cnn". Moreover, we aim to model the changing trends of the connections, such as "cnn" becomes highly related to "imagenet" after 2010, which enables the tracking of knowledge evolutions. In this work, to facilitate such a novel insightful search system, we propose \textsc{SetEvolve}, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora. Systematic experiments on synthetic data and insightful case studies on real-world corpora demonstrate the utility of \textsc{SetEvolve}.
Clustering Strategies of Cooperative Adaptive Cruise Control: Impacts on Human-driven Vehicles
Venue: arXiv cs.MA 2019
Year: 2019

As a promising application of connected and automated vehicles (CAVs), Cooperative Adaptive Cruise Control (CACC) is expected to be deployed on the public road in the near term. Thus far the majority of the CACC studies have been focusing on the overall network performance with limited insight on the potential impact of CAVs on human-driven vehicles (HVs). This paper aims to quantify the influence of CAVs on HVs by studying the high-resolution vehicle trajectory data that is obtained from microscopic simulation. Two clustering strategies for CACC are implemented: an ad hoc coordination one and a local coordination one. Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates (MPRs) in terms of network throughput and productivity. The greatest performance difference between the two strategies is observed at 30% and 40% MPR for throughput and productivity, respectively. However, the distributions of the hard braking observations (as a potential safety impact) for HVs change significantly under local coordination strategy. Regardless of the clustering strategy, CAVs increase the average lane change frequency for HVs. 30% MPR is the break-even point for local coordination, after which the average lane change frequency decreases from the peak 5.42 to 5.38. Such inverse relationship to MPR is not found in the ah hoc case and the average lane change frequency reaches the highest 5.48 at 40% MPR.
Salient Instance Segmentation via Subitizing and Clustering
Venue: arXiv cs.CV 2019
Year: 2019

The goal of salient region detection is to identify the regions of an image that attract the most attention. Many methods have achieved state-of-the-art performance levels on this task. Recently, salient instance segmentation has become an even more challenging task than traditional salient region detection; however, few of the existing methods have concentrated on this underexplored problem. Unlike the existing methods, which usually employ object proposals to roughly count and locate object instances, our method applies salient objects subitizing to predict an accurate number of instances for salient instance segmentation. In this paper, we propose a multitask densely connected neural network (MDNN) to segment salient instances in an image. In contrast to existing approaches, our framework is proposal-free and category-independent. The MDNN contains two parallel branches: the first is a densely connected subitizing network (DSN) used for subitizing prediction; the second is a densely connected fully convolutional network (DFCN) used for salient region detection. The MDNN simultaneously outputs saliency maps and salient object subitizing. Then, an adaptive deep feature-based spectral clustering operation segments the salient regions into instances based on the subitizing and saliency maps. The experimental results on both salient region detection and salient instance segmentation datasets demonstrate the satisfactory performance of our framework. Notably, its APr@0.5 and Apr@0.7 reaches 73.46% and 60.14% in the salient instance dataset, substantially higher than the results achieved by the state-of-the-art algorithm.
Service Embedding in IoT Networks
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Things is anticipated to participate in the execution of a variety of complex tasks in the near future. IoT objects capable of handling multiple sensing and actuating functions are the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. These requests are characterised by the different requirements of sensing actuating functions, processing and memory needs, activation zones, latency, etc. In service-oriented architecture-based IoT, application requests are translated into a business process BP workflow. In this study, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and location where sensing or actuation are needed. The virtual links capture the requested communication requirements between nodes. In this paper, we introduce a framework, optimised using mixed integer linear programming MILP, that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. The proposed framework results in a physical plan that optimally allocates the processing needed and provisions the sensing and actuation at the required locations requirements to an appropriate set of IoT nodes. The optimisation goal is to minimise the IoT layer total power consumption and optimise the traffic distribution in a manner that minimises the traffic latency of each IoT node. Our results show that the proposed framework enhances the network performance by reducing the power consumption and latency.
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
Venue: arXiv cs.LG 2019
Year: 2019

We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.
Modeling Single Picker Routing Problems in Classical and Modern Warehouses
Venue: arXiv cs.DS 2019
Year: 2019

The standard single picker routing problem (SPRP) seeks the cost-minimal tour to collect a set of given articles in a rectangular single-block warehouse with parallel picking aisles and a dedicated storage policy, i.e, each SKU is only available from one storage location in the warehouse. We present a compact formulation that forgoes classical subtour elimination constraints by directly exploiting two of the properties of an optimal picking tour used in the dynamic programming algorithm of Ratliff and Rosenthal (1983). We extend the formulation to three important settings prevalent in modern e-commerce warehouses: scattered storage, decoupling of picker and cart, and multiple end depots. In numerical studies, our formulation outperforms existing standard SPRP formulations from the literature and proves able to solve large instances within short runtimes. Realistically sized instances of the three problem extensions can also be solved with low computational effort. We find that decoupling of picker and cart can lead to substantial cost savings depending on the speed and capacity of the picker when traveling alone, whereas additional end depots have rather limited benefits in a single-block warehouse.
Parallel Machine Scheduling to Minimize Energy Consumption
Venue: arXiv cs.DS 2019
Year: 2019

Given n jobs with release dates, deadlines and processing times we consider the problem of scheduling them on m parallel machines so as to minimize the total energy consumed. Machines can enter a sleep state and they consume no energy in this state. Each machine requires Q units of energy to awaken from the sleep state and in its active state the machine can process jobs and consumes a unit of energy per unit time. We allow for preemption and migration of jobs and provide the first constant approximation algorithm for this problem.
Siamese Neural Networks for Wireless Positioning and Channel Charting
Venue: arXiv cs.LG 2019
Year: 2019

Neural networks have been proposed recently for positioning and channel charting of user equipments (UEs) in wireless systems. Both of these approaches process channel state information (CSI) that is acquired at a multi-antenna base-station in order to learn a function that maps CSI to location information. CSI-based positioning using deep neural networks requires a dataset that contains both CSI and associated location information. Channel charting (CC) only requires CSI information to extract relative position information. Since CC builds on dimensionality reduction, it can be implemented using autoencoders. In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting. In addition, our framework enables semisupervised positioning, where only a small set of location information is available during training. We use simulations to demonstrate that Siamese networks achieve similar or better performance than existing positioning and CC approaches with a single, unified neural network architecture.
Understanding and Improving Proximity Graph based Maximum Inner Product Search
Venue: arXiv cs.IR 2019
Year: 2019

The inner-product navigable small world graph (ip-NSW) represents the state-of-the-art method for approximate maximum inner product search (MIPS) and it can achieve an order of magnitude speedup over the fastest baseline. However, to date it is still unclear where its exceptional performance comes from. In this paper, we show that there is a strong norm bias in the MIPS problem, which means that the large norm items are very likely to become the result of MIPS. Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem - large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items. Furthermore, we propose the ip-NSW+ algorithm, which improves ip-NSW by introducing an additional angular proximity graph. Search is first conducted on the angular graph to find the angular neighbors of a query and then the MIPS neighbors of these angular neighbors are used to initialize the candidate pool for search on the inner-product proximity graph. Experiment results show that ip-NSW+ consistently and significantly outperforms ip-NSW and provides more robust performance under different data distributions.