pwc-archive/papers-with-abstracts huggingface dataset:
CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data
Authors: Qi Yan, Jianhao Zheng, Simon Reding, Shanci Li, Iordan Doytchinov
Date: 2021-12-16 00:00:00
Proceeding: CVPR 2022 1
Tasks: Pose Estimation, Representation Learning, Synthetic Data Generation, Visual Localization

We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data. Despite significant progress in recent years, most learning-based approaches to visual localization target at a single domain and require a dense database of geo-tagged images to function well. To mitigate the data scarcity issue and improve the scalability of the neural localization models, we introduce TOPO-DataGen, a versatile synthetic data generation tool that traverses smoothly between the real and virtual world, hinged on the geographic camera viewpoint. New large-scale sim-to-real benchmark datasets are proposed to showcase and evaluate the utility of the said synthetic data. Our experiments reveal that synthetic data generically enhances the neural network performance on real data. Furthermore, we introduce CrossLoc, a cross-modal visual representation learning approach to pose estimation that makes full use of the scene coordinate ground truth via self-supervision. Without any extra data, CrossLoc significantly outperforms the state-of-the-art methods and achieves substantially higher real-data sample efficiency. Our code and datasets are all available at https://crossloc.github.io/.
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Authors: Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Date: 2021-12-16 00:00:00
Tasks: Bayesian Inference, Decision Making, parameter estimation, Probabilistic Deep Learning

Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow framework uses a two-step approach to enable amortized parameter estimation in settings where the likelihood function is implicitly defined by a simulation program. But how faithful is such inference when simulations are poor representations of reality? In this paper, we conceptualize the types of model misspecification arising in simulation-based inference and systematically investigate the performance of the BayesFlow framework under these misspecifications. We propose an augmented optimization objective which imposes a probabilistic structure on the latent data space and utilize maximum mean discrepancy (MMD) to detect potentially catastrophic misspecifications during inference undermining the validity of the obtained results. We verify our detection criterion on a number of artificial and realistic misspecifications, ranging from toy conjugate models to complex models of decision making and disease outbreak dynamics applied to real data. Further, we show that posterior inference errors increase as a function of the distance between the true data-generating distribution and the typical set of simulations in the latent summary space. Thus, we demonstrate the dual utility of MMD as a method for detecting model misspecification and as a proxy for verifying the faithfulness of amortized Bayesian inference.
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
Authors: Jie Qin, Jie Wu, Xuefeng Xiao, Lujun Li, Xingang Wang
Date: 2021-12-16 00:00:00
Tasks: Feature Importance, Scene Understanding, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. Specifically, an attention modulation module (AMM) is employed to rearrange the distribution of feature importance from the channel-spatial sequential perspective, which helps to explicitly model channel-wise interdependencies and spatial encodings to adaptively modulate segmentation-oriented activation responses. Furthermore, we introduce a cross pseudo supervision for dual branches, which can be regarded as a semantic similar regularization to mutually refine two branches. Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance.
基于UWB的室内精确定位算法研究
Authors: 刘洋
Date: 2021-02-15 00:00:00
Proceeding: 硕士电子期刊出版信息 2021 2

近年来,随着无线定位技术的不断进步和发展,全球卫星导航定位技术已日渐趋于完善和成熟。但由于受到建筑物遮挡、极恶劣天气等因素的影响,卫星导航定位技术已不能满足人们对于室内精确定位的需求,因此,为了提高室内位置服务质量,室内定位技术应运而生。在室内定位技术中,超宽带（UWB）定位技术因具有定位精度高、传输速率快等优点被人们广泛应用。本文在此背景下对UWB的室内精确定位算法进行深入研究。本文从超宽带定位技术的理论出发,分析了超宽带信号的特点及优势,研究了超宽带脉冲信号的调制方式以及超宽带信道模型,重点仿真了2-10GHz频段的超宽带室内信道模型。在超宽带信道模型的基础上,本文对比分析了四种室内定位方法及其基本原理,得出到达时间差法（TDOA）更适用于UWB室内定位。针对如何求解TDOA定位方法的双曲线模型,本文研究了四种求解非线性双曲线方程组的算法,通过对仿真结果分析,发现Chan氏算法在UWB定位中占有一定的优势。针对Chan氏算法的解是模糊解的问题,本文使用最大似然估计法进行了消除。为了进一步提高定位精度,本文在Chan氏算法的基础上提出了一种基于TDOA的Chan-BFGS定位算法,在相同的参考节点及传播环境下对其定位算法进行了仿真分析,结果表明该定位算法跟Chan氏算法相比定位精度提高了60%。针对在室内环境下非视距误差导致定位精度下降的问题,本文通过对比分析几种非线性卡尔曼滤波的定位效果后,提出了利用改进的容积卡尔曼滤波算法（CKF）进行优化定位,该算法在CKF算法的基础上通过设置阈值对新息值进行了修正,同时通过奇异值分解法（SVD）对CKF算法的复杂度进行了简化。本文对改进CKF算法进行了性能仿真分析,结果表明改进的CKF算法可以很好的抑制非视距误差,能够对移动节点进行精确定位和跟踪。
Solving Inverse Problems with NerfGANs
Authors: Giannis Daras, Wen-Sheng Chu, Abhishek Kumar, Dmitry Lagun, Alexandros G. Dimakis
Date: 2021-12-16 00:00:00
Tasks: Attribute, NeRF

We introduce a novel framework for solving inverse problems using NeRF-style generative models. We are interested in the problem of 3-D scene reconstruction given a single 2-D image and known camera parameters. We show that naively optimizing the latent space leads to artifacts and poor novel view rendering. We attribute this problem to volume obstructions that are clear in the 3-D geometry and become visible in the renderings of novel views. We propose a novel radiance field regularization method to obtain better 3-D surfaces and improved novel views given single view observations. Our method naturally extends to general inverse problems including inpainting where one observes only partially a single view. We experimentally evaluate our method, achieving visual improvements and performance boosts over the baselines in a wide range of tasks. Our method achieves $30-40\%$ MSE reduction and $15-25\%$ reduction in LPIPS loss compared to the previous state of the art.
Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network
Authors: Rytis Augustauskas, Arūnas Lipnickas, Tadas Surgailis
Date: 2021-05-23 00:00:00
Proceeding: MDPI Sensors 2021 5
Tasks: 2D Semantic Segmentation, Decoder, object-detection, Object Detection, Segmentation, Semantic Segmentation, UNET Segmentation

Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations’ semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model’s Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
Authors: Diptesh Kanojia, Raj Dabre, Shubham Dewangan, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
Date: 2021-12-16 00:00:00
Proceeding: COLING 2020 8
Tasks: Cross-Lingual Information Retrieval, Cross-Lingual Word Embeddings, Information Retrieval, Machine Translation, NMT, Retrieval, Translation, Word Embeddings

Cognates are variants of the same lexical form across different languages; for example 'fonema' in Spanish and 'phoneme' in English are cognates, both of which mean 'a unit of sound'. The task of automatic detection of cognates among any two languages can help downstream NLP tasks such as Cross-lingual Information Retrieval, Computational Phylogenetics, and Machine Translation. In this paper, we demonstrate the use of cross-lingual word embeddings for detecting cognates among fourteen Indian Languages. Our approach introduces the use of context from a knowledge graph to generate improved feature representations for cognate detection. We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task. We evaluate our methods to detect cognates on a challenging dataset of twelve Indian languages, namely, Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam. Additionally, we create evaluation datasets for two more Indian languages, Konkani and Nepali. We observe an improvement of up to 18% points, in terms of F-score, for cognate detection. Furthermore, we observe that cognates extracted using our method help improve NMT quality by up to 2.76 BLEU. We also release our code, newly constructed datasets and cross-lingual models publicly.
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs
Authors: Ezgi Korkmaz
Date: 2021-12-16 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sensitivity

The use of deep neural networks as function approximators has led to striking progress for reinforcement learning algorithms and applications. Yet the knowledge we have on decision boundary geometry and the loss landscape of neural policies is still quite limited. In this paper we propose a framework to investigate the decision boundary and loss landscape similarities across states and across MDPs. We conduct experiments in various games from Arcade Learning Environment, and discover that high sensitivity directions for neural policies are correlated across MDPs. We argue that these high sensitivity directions support the hypothesis that non-robust features are shared across training environments of reinforcement learning agents. We believe our results reveal fundamental properties of the environments used in deep reinforcement learning training, and represent a tangible step towards building robust and reliable deep reinforcement learning agents.
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation
Authors: Enrico Marchesini, Alessandro Farinelli
Date: 2021-12-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot considers its path without explicitly sharing observations with other robots and can lead to non-stationary issues in Deep Reinforcement Learning (DRL). The typical CTDE algorithm factorizes the joint action-value function into individual ones, to favor cooperation and achieve decentralized execution. Such factorization involves constraints (e.g., monotonicity) that limit the emergence of novel behaviors in an individual as each agent is trained starting from a joint action-value. In contrast, we propose a novel architecture for CTDE that uses a centralized state-value network to compute a joint state-value, which is used to inject global state information in the value-based updates of the agents. Consequently, each model computes its gradient update for the weights, considering the overall state of the environment. Our idea follows the insights of Dueling Networks as a separate estimation of the joint state-value has both the advantage of improving sample efficiency, while providing each robot information whether the global state is (or is not) valuable. Experiments in a robotic navigation task with 2 4, and 8 robots, confirm the superior performance of our approach over prior CTDE methods (e.g., VDN, QMIX).
Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types
Authors: T Y S S Santosh, Prantika Chakraborty, Sudakshina Dutta, Debarshi Kumar Sanyal, Partha Pratim Das
Date: 2021-09-30 00:00:00
Proceeding: Extraction and Evaluation of Knowledge Entities from Scientific Documents 2021 9
Tasks: Articles, Joint Entity and Relation Extraction, Joint Entity and Relation Extraction on Scientific Data, POS, Relation, Relation Extraction, Sentence

Scientific articles contain various types of domain-specific entities and relations between them. The entities and their relations
succinctly capture important information about the topic of the
document and hence, they are crucial to the understanding and
automatic analysis of the documents. In this paper, we aim to automatically extract entities and relations from a scientific abstract
using a deep neural model. Given an input sentence, we use a
pretrained transformer to produce contextual embeddings of the
tokens which are then enriched with embeddings of their part-of-speech (POS) tags. A sequence of enriched token representations
forms a span, and entities and relations are jointly learned over
spans. Entity logits predicted by the entity classifier are used as
features in the relation classifier. Our proposed model improves
upon competitive baselines in the literature for entity and relation
extraction on SciERC and ADE datasets.
NSAC: A Novel Clustering Protocol in Cognitive Radio Sensor Networks for Internet of Things
Authors: M. Zheng, S. Chen, W. Liang and M. Song
Date: 2019-02-08 00:00:00
Proceeding: IEEE Internet of Things Journal 2019 2
Tasks: Clustering

Clustering is an effective method to manage communications in cognitive radio sensor networks (CRSNs). This letter proposes a network stability-aware clustering (NSAC) protocol for CRSNs. Spectrum dynamics and energy consumption are for the first time simultaneously integrated into the protocol design of NSAC. Extensive simulations show that the proposed NSAC protocol obviously outperforms existing methods in the aspects of network stability and energy consumption.
A molecular generative model with genetic algorithm and tree search for cancer samples
Authors: Sejin Park, Hyunju Lee
Date: 2021-12-16 00:00:00
Tasks: Drug Discovery

Personalized medicine is expected to maximize the intended drug effects and minimize side effects by treating patients based on their genetic profiles. Thus, it is important to generate drugs based on the genetic profiles of diseases, especially in anticancer drug discovery. However, this is challenging because the vast chemical space and variations in cancer properties require a huge time resource to search for proper molecules. Therefore, an efficient and fast search method considering genetic profiles is required for de novo molecular design of anticancer drugs. Here, we propose a faster molecular generative model with genetic algorithm and tree search for cancer samples (FasterGTS). FasterGTS is constructed with a genetic algorithm and a Monte Carlo tree search with three deep neural networks: supervised learning, self-trained, and value networks, and it generates anticancer molecules based on the genetic profiles of a cancer sample. When compared to other methods, FasterGTS generated cancer sample-specific molecules with general chemical properties required for cancer drugs within the limited numbers of samplings. We expect that FasterGTS contributes to the anticancer drug generation.
Search for temporal cell segmentation robustness in phase-contrast microscopy videos
Authors: Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia
Date: 2021-12-16 00:00:00
Tasks: Cell Segmentation, Segmentation, Transfer Learning

Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices and imaged with phase-contrast microscopy. Our approach uses transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information from the past and provide a consistent segmentation result. Lastly, we propose a geometrical-characterization approach to studying cancer cell morphology. Our approach provides stable results in time, and it is robust to the different weight initialization or training data sampling. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.
MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection
Authors: Chengbo Dong, Xinru Chen, Ruohan Hu, Juan Cao, Xirong Li
Date: 2021-12-16 00:00:00
Tasks: Image Manipulation, Image Manipulation Detection, Image Manipulation Localization, Semantic Segmentation, Specificity

As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods are promising when training and test data are well aligned, but perform poorly on independent tests. Moreover, due to the absence of authentic test images, their image-level detection specificity is in doubt. The key question is how to design and train a deep neural network capable of learning generalizable features sensitive to manipulations in novel data, whilst specific to prevent false alarms on the authentic. We propose multi-view feature learning to jointly exploit tampering boundary artifacts and the noise view of the input image. As both clues are meant to be semantic-agnostic, the learned features are thus generalizable. For effectively learning from authentic images, we train with multi-scale (pixel / edge / image) supervision. We term the new network MVSS-Net and its enhanced version MVSS-Net++. Experiments are conducted in both within-dataset and cross-dataset scenarios, showing that MVSS-Net++ performs the best, and exhibits better robustness against JPEG compression, Gaussian blur and screenshot based image re-capturing.
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
Authors: Trevor Ablett, Bryan Chan, Jonathan Kelly
Date: 2021-12-16 00:00:00
Tasks: Imitation Learning, Transfer Learning

Effective exploration continues to be a significant challenge that prevents the deployment of reinforcement learning for many physical systems. This is particularly true for systems with continuous and high-dimensional state and action spaces, such as robotic manipulators. The challenge is accentuated in the sparse rewards setting, where the low-level state information required for the design of dense rewards is unavailable. Adversarial imitation learning (AIL) can partially overcome this barrier by leveraging expert-generated demonstrations of optimal behaviour and providing, essentially, a replacement for dense reward information. Unfortunately, the availability of expert demonstrations does not necessarily improve an agent's capability to explore effectively and, as we empirically show, can lead to inefficient or stagnated learning. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of, in addition to a main task, multiple auxiliary tasks. Subsequently, a hierarchical model is used to learn each task reward and policy through a modified AIL procedure, in which exploration of all tasks is enforced via a scheduler composing different tasks together. This affords many benefits: learning efficiency is improved for main tasks with challenging bottleneck transitions, expert data becomes reusable between tasks, and transfer learning through the reuse of learned auxiliary task models becomes possible. Our experimental results in a challenging multitask robotic manipulation domain indicate that our method compares favourably to supervised imitation learning and to a state-of-the-art AIL method. Code is available at https://github.com/utiasSTARS/lfgp.
The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networks
Authors: Enrico Giudice, Jack Kuipers, Giusi Moffa
Date: 2021-12-16 00:00:00

Learning the graphical structure of Bayesian networks is key to describing data-generating mechanisms in many complex applications but poses considerable computational challenges. Observational data can only identify the equivalence class of the directed acyclic graph underlying a Bayesian network model, and a variety of methods exist to tackle the problem. Under certain assumptions, the popular PC algorithm can consistently recover the correct equivalence class by reverse-engineering the conditional independence (CI) relationships holding in the variable distribution. The dual PC algorithm is a novel scheme to carry out the CI tests within the PC algorithm by leveraging the inverse relationship between covariance and precision matrices. By exploiting block matrix inversions we can also perform tests on partial correlations of complementary (or dual) conditioning sets. The multiple CI tests of the dual PC algorithm proceed by first considering marginal and full-order CI relationships and progressively moving to central-order ones. Simulation studies show that the dual PC algorithm outperforms the classic PC algorithm both in terms of run time and in recovering the underlying network structure, even in the presence of deviations from Gaussianity. Additionally, we show that the dual PC algorithm applies for Gaussian copula models, and demonstrate its performance in that setting.