pwc-archive/papers-with-abstracts huggingface dataset:
Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension
Authors: Felix Nobis, Odysseas Papanikolaou, Johannes Betz, Markus Lienkamp
Date: 2020-05-15 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Simultaneous Localization and Mapping, Visual Localization

Electric vhicles and autonomous driving dominate current research efforts in the automotive sector. The two topics go hand in hand in terms of enabling safer and more environmentally friendly driving. One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map. In this paper, we make use of a stereo camera sensor in order to perceive the environment and create the map. With live Simultaneous Localization and Mapping (SLAM), there is a risk of mislocalization, since no ground truth map is used as a reference and errors accumulate over time. Therefore, we first build up and save a map of visual features of the environment at low driving speeds with our extension to the ORB-SLAM\,2 package. In a second run, we reload the map and then localize on the previously built-up map. Loading and localizing on a previously built map can improve the continuous localization accuracy for autonomous vehicles in comparison to a full SLAM. This map saving feature is missing in the original ORB-SLAM\,2 implementation. We evaluate the localization accuracy for scenes of the KITTI dataset against the built up SLAM map. Furthermore, we test the localization on data recorded with our own small scale electric model car. We show that the relative translation error of the localization stays under 1\% for a vehicle travelling at an average longitudinal speed of 36 m/s in a feature-rich environment. The localization mode contributes to a better localization accuracy and lower computational load compared to a full SLAM. The source code of our contribution to the ORB-SLAM2 will be made public at: https://github.com/TUMFTM/orbslam-map-saving-extension.
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
Authors: Felix Nobis, Maximilian Geisslinger, Markus Weber, Johannes Betz, Markus Lienkamp
Date: 2020-05-15 00:00:00
Proceeding: 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF) 2019 10
Tasks: 2D Object Detection, object-detection, Object Detection, Sensor Fusion

Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusionNet (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result. Additionally, we introduce BlackIn, a training strategy inspired by Dropout, which focuses the learning on a specific sensor type. We show that the fusion network is able to outperform a state-of-the-art image-only network for two different datasets. The code for this research will be made available to the public at: https://github.com/TUMFTM/CameraRadarFusionNet.
PrimiTect: Fast Continuous Hough Voting for Primitive Detection
Authors: Christiane Sommer, Yumin Sun, Erik Bylow, Daniel Cremers
Date: 2020-05-15 00:00:00

This paper tackles the problem of data abstraction in the context of 3D point sets. Our method classifies points into different geometric primitives, such as planes and cones, leading to a compact representation of the data. Being based on a semi-global Hough voting scheme, the method does not need initialization and is robust, accurate, and efficient. We use a local, low-dimensional parameterization of primitives to determine type, shape and pose of the object that a point belongs to. This makes our algorithm suitable to run on devices with low computational power, as often required in robotics applications. The evaluation shows that our method outperforms state-of-the-art methods both in terms of accuracy and robustness.
Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
Authors: Jun Liu, Xue-Cheng Tai, Shousheng Luo
Date: 2020-05-15 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Convex Shapes (CS) are common priors for optic disc and cup segmentation in eye fundus images. It is important to design proper techniques to represent convex shapes. So far, it is still a problem to guarantee that the output objects from a Deep Neural Convolution Networks (DCNN) are convex shapes. In this work, we propose a technique which can be easily integrated into the commonly used DCNNs for image segmentation and guarantee that outputs are convex shapes. This method is flexible and it can handle multiple objects and allow some of the objects to be convex. Our method is based on the dual representation of the sigmoid activation function in DCNNs. In the dual space, the convex shape prior can be guaranteed by a simple quadratic constraint on a binary representation of the shapes. Moreover, our method can also integrate spatial regularization and some other shape prior using a soft thresholding dynamics (STD) method. The regularization can make the boundary curves of the segmentation objects to be simultaneously smooth and convex. We design a very stable active set projection algorithm to numerically solve our model. This algorithm can form a new plug-and-play DCNN layer called CS-STD whose outputs must be a nearly binary segmentation of convex objects. In the CS-STD block, the convexity information can be propagated to guide the DCNN in both forward and backward propagation during training and prediction process. As an application example, we apply the convexity prior layer to the retinal fundus images segmentation by taking the popular DeepLabV3+ as a backbone network. Experimental results on several public datasets show that our method is efficient and outperforms the classical DCNN segmentation methods.
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
Authors: Xin Xia, Xuefeng Xiao, Xing Wang, Min Zheng
Date: 2020-05-15 00:00:00
Tasks: Diversity, Efficient Neural Network, Neural Architecture Search

Neural Architecture Search (NAS) has attracted growing interest. To reduce the search cost, recent work has explored weight sharing across models and made major progress in One-Shot NAS. However, it has been observed that a model with higher one-shot model accuracy does not necessarily perform better when stand-alone trained. To address this issue, in this paper, we propose Progressive Automatic Design of search space, named PAD-NAS. Unlike previous approaches where the same operation search space is shared by all the layers in the supernet, we formulate a progressive search strategy based on operation pruning and build a layer-wise operation search space. In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity. During the search, we also take the hardware platform constraints into consideration for efficient neural network model deployment. Extensive experiments on ImageNet show that our method can achieve state-of-the-art performance.
Guided interactive image segmentation using machine learning and color based data set clustering
Authors: Adrian Friebel, Tim Johann, Dirk Drasdo, Stefan Hoehme
Date: 2020-05-15 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Colorization, Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larger numbers of images thereby enabling interactive image analysis for recent new technological advances in high-throughput imaging. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general.
SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
Authors: Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Taejin L. Min, Shiv Bhanu, Bir Bhanu
Date: 2020-05-14 00:00:00
Tasks: Attribute, Prognosis

While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on availability of large quantities of data. Medical image data, however, is scarce and fragmented. Generative Adversarial Networks (GANs) have recently been very effective in handling such datasets by generating more data. If the datasets are very small, however, GANs cannot learn the data distribution properly, resulting in less diverse or low-quality results. One such limited dataset is that for the concurrent gain of 19 and 20 chromosomes (19/20 co-gain), a mutation with positive prognostic value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for the mutation to streamline the extensive and invasive prognosis pipeline. Since this mutation is relatively rare, i.e. small dataset, we propose a novel generative framework - the Sequential Attribute GEnerator (SAGE), that generates detailed tumor imaging features while learning from a limited dataset. Experiments show that not only does SAGE generate high quality tumors when compared to standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers accurately.
Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement
Authors: Xiaoxiao Li, Xiaopeng Guo, Liye Mei, Mingyu Shang, Jie Gao, Maojing Shu, Xiang Wang
Date: 2020-05-15 00:00:00
Tasks: Computational Efficiency, Image Enhancement, Low-Light Image Enhancement

Low-light image enhancement is a promising solution to tackle the problem of insufficient sensitivity of human vision system (HVS) to perceive information in low light environments. Previous Retinex-based works always accomplish enhancement task by estimating light intensity. Unfortunately, single light intensity modelling is hard to accurately simulate visual perception information, leading to the problems of imbalanced visual photosensitivity and weak adaptivity. To solve these problems, we explore the precise relationship between light source and visual perception and then propose the visual perception (VP) model to acquire a precise mathematical description of visual perception. The core of VP model is to decompose the light source into light intensity and light spatial distribution to describe the perception process of HVS, offering refinement estimation of illumination and reflectance. To reduce complexity of the estimation process, we introduce the rapid and adaptive $\mathbf{\beta}$ and $\mathbf{\gamma}$ functions to build an illumination and reflectance estimation scheme. Finally, we present a optimal determination strategy, consisting of a \emph{cycle operation} and a \emph{comparator}. Specifically, the \emph{comparator} is responsible for determining the optimal enhancement results from multiple enhanced results through implementing the \emph{cycle operation}. By coordinating the proposed VP model, illumination and reflectance estimation scheme, and the optimal determination strategy, we propose a rapid and adaptive framework for low-light image enhancement. Extensive experiment results demenstrate that the proposed method achieves better performance in terms of visual comparison, quantitative assessment, and computational efficiency, compared with the currently state-of-the-arts.
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
Authors: Akella Ravi Tej, Shirsendu Sukanta Halder, Arunav Pratap Shandeelya, Vinod Pankajakshan
Date: 2020-05-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, valid

Single image super-resolution (SISR) is an ill-posed problem with an indeterminate number of valid solutions. Solving this problem with neural networks would require access to extensive experience, either presented as a large training set over natural images or a condensed representation from another pre-trained network. Perceptual loss functions, which belong to the latter category, have achieved breakthrough success in SISR and several other computer vision tasks. While perceptual loss plays a central role in the generation of photo-realistic images, it also produces undesired pattern artifacts in the super-resolved outputs. In this paper, we show that the root cause of these pattern artifacts can be traced back to a mismatch between the pre-training objective of perceptual loss and the super-resolution objective. To address this issue, we propose to augment the existing perceptual loss formulation with a novel content loss function that uses the latent features of a discriminator network to filter the unwanted artifacts across several levels of adversarial similarity. Further, our modification has a stabilizing effect on non-convex optimization in adversarial training. The proposed approach offers notable gains in perceptual quality based on an extensive human evaluation study and a competent reconstruction fidelity when tested on objective evaluation metrics.
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
Authors: Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek
Date: 2020-05-15 00:00:00
Tasks: Image Registration

This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer. The CT images were refined prior to registration by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of VoxelMorph when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images. In a 4-fold cross-validation scheme, the incremental training strategy achieved significantly better registration performance compared to training on a single volume. Although our deformable image registration method did not outperform iterative registration using NiftyReg (considered as a benchmark) in terms of registration quality, the registrations were approximately 300 times faster. This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.
Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders
Authors: Baurzhan Muminov, Luat T. Vuong
Date: 2020-05-15 00:00:00

We introduce a vortex phase transform with a lenslet-array to accompany shallow, dense, ``small-brain'' neural networks for high-speed and low-light imaging. Our single-shot ptychographic approach exploits the coherent diffraction, compact representation, and edge enhancement of Fourier-tranformed spiral-phase gradients. With vortex spatial encoding, a small brain is trained to deconvolve images at rates 5-20 times faster than those achieved with random encoding schemes, where greater advantages are gained in the presence of noise. Once trained, the small brain reconstructs an object from intensity-only data, solving an inverse mapping without performing iterations on each image and without deep-learning schemes. With this hybrid, optical-digital, vortex Fourier encoded, small-brain scheme, we reconstruct MNIST Fashion objects illuminated with low-light flux (5 nJ/cm$^2$) at a rate of several thousand frames per second on a 15 W central processing unit, two orders of magnitude faster than convolutional neural networks.
Exploring Crowd Co-creation Scenarios for Sketches
Authors: Devi Parikh, C. Lawrence Zitnick
Date: 2020-05-15 00:00:00

As a first step towards studying the ability of human crowds and machines to effectively co-create, we explore several human-only collaborative co-creation scenarios. The goal in each scenario is to create a digital sketch using a simple web interface. We find that settings in which multiple humans iteratively add strokes and vote on the best additions result in the sketches with highest perceived creativity (value + novelty). Lack of collaboration leads to a higher variance in quality and lower novelty or surprise. Collaboration without voting leads to high novelty but low quality.
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Authors: Jiaoyang Li, Andrew Tinka, Scott Kiesel, Joseph W. Durham, T. K. Satish Kumar, Sven Koenig
Date: 2020-05-15 00:00:00
Tasks: Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.
MineReduce: an approach based on data mining for problem size reduction
Authors: Marcelo Rodrigues de Holanda Maia, Alexandre Plastino, Puca Huachi Vaz Penna
Date: 2020-05-15 00:00:00
Tasks: Combinatorial Optimization

Hybrid variations of metaheuristics that include data mining strategies have been utilized to solve a variety of combinatorial optimization problems, with superior and encouraging results. Previous hybrid strategies applied mined patterns to guide the construction of initial solutions, leading to more effective exploration of the solution space. Solving a combinatorial optimization problem is usually a hard task because its solution space grows exponentially with its size. Therefore, problem size reduction is also a useful strategy in this context, especially in the case of large-scale problems. In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction. We present an application of MineReduce to improve a heuristic for the heterogeneous fleet vehicle routing problem. The results obtained in computational experiments show that this proposed heuristic demonstrates superior performance compared to the original heuristic and other state-of-the-art heuristics, achieving better solution costs with shorter run times.
An Object Model for the Representation of Empirical Knowledge
Authors: Joël Colloc, Danielle Boulanger
Date: 2020-05-15 00:00:00
Tasks: Object, Vocal Bursts Type Prediction

We are currently designing an object oriented model which describes static and dynamical knowledge in diff{\'e}rent domains. It provides a twin conceptual level. The internal level proposes: the object structure composed of sub-objects hierarchy, structure evolution with dynamical functions, same type objects comparison with evaluation functions. It uses multiple upward inheritance from sub-objects properties to the Object. The external level describes: object environment, it enforces object types and uses external simple inheritance from the type to the sub-types.
A Transformer-based Embedding Model for Personalized Product Search
Authors: Keping Bi, Qingyao Ai, W. Bruce Croft
Date: 2020-05-18 00:00:00
Tasks: Retrieval

Product search is an important way for people to browse and purchase items on E-commerce platforms. While customers tend to make choices based on their personal tastes and preferences, analysis of commercial product search logs has shown that personalization does not always improve product search quality. Most existing product search techniques, however, conduct undifferentiated personalization across search sessions. They either use a fixed coefficient to control the influence of personalization or let personalization take effect all the time with an attention mechanism. The only notable exception is the recently proposed zero-attention model (ZAM) that can adaptively adjust the effect of personalization by allowing the query to attend to a zero vector. Nonetheless, in ZAM, personalization can act at most as equally important as the query and the representations of items are static across the collection regardless of the items co-occurring in the user's historical purchases. Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user's purchase history with a transformer architecture. Personalization could have a dominant impact when necessary and interactions between items can be taken into consideration when computing attention weights. Experimental results show that TEM outperforms state-of-the-art personalization product retrieval models significantly.
Reconstructing Maps from Text
Authors: Johnathan E. Avery, Robert L. Goldstone, Michael N. Jones
Date: 2020-05-18 00:00:00

Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding (De Vega et al., 2012). In this paper we investigate the statistical sources required in language to infer maps, and resulting constraints placed on mechanisms of semantic representation. Study 1 brings word co-occurrence under experimental control to demonstrate that direct co-occurrence in language is necessary for traditional DSMs to successfully reproduce maps. Study 2 presents an instance-based DSM that is capable of reconstructing maps independent of the frequency of co-occurrence of city names.