pwc-archive/papers-with-abstracts huggingface dataset:
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks
Authors: Léopold Cambier, Anahita Bhiwandiwalla, Ting Gong, Mehran Nekuii, Oguz H. Elibol, Hanlin Tang
Date: 2020-01-16 00:00:00
Proceeding: ICLR 2020 1
Tasks: Quantization

Training with larger number of parameters while keeping fast iterations is an increasingly adopted strategy and trend for developing better performing Deep Neural Network (DNN) models. This necessitates increased memory footprint and computational requirements for training. Here we introduce a novel methodology for training deep neural networks using 8-bit floating point (FP8) numbers. Reduced bit precision allows for a larger effective memory and increased computational speed. We name this method Shifted and Squeezed FP8 (S2FP8). We show that, unlike previous 8-bit precision training methods, the proposed method works out-of-the-box for representative models: ResNet-50, Transformer and NCF. The method can maintain model accuracy without requiring fine-tuning loss scaling parameters or keeping certain layers in single precision. We introduce two learnable statistics of the DNN tensors - shifted and squeezed factors that are used to optimally adjust the range of the tensors in 8-bits, thus minimizing the loss in information due to quantization.
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Authors: Hsu-kuang Chiu, Antonio Prioletti, Jie Li, Jeannette Bohg
Date: 2020-01-16 00:00:00
Tasks: 3D Multi-Object Tracking, Autonomous Driving, Multi-Object Tracking, Object, Object Tracking

3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019. Our method estimates the object states by adopting a Kalman Filter. We initialize the state covariance as well as the process and observation noise covariance with statistics from the training set. We also use the stochastic information from the Kalman Filter in the data association step by measuring the Mahalanobis distance between the predicted object states and current object detections. Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method by a large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric.
AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion
Authors: Trung Q. Tran
Date: 2020-01-16 00:00:00
Tasks: Sentence

I introduce a simple but efficient method to solve one of the critical aspects of English grammar which is the relationship between active sentence and passive sentence. In fact, an active sentence and its corresponding passive sentence express the same meaning, but their structure is different. I utilized Prolog [4] along with Definite Clause Grammars (DCG) [5] for doing the conversion between active sentence and passive sentence. Some advanced techniques were also used such as Extra Arguments, Extra Goals, Lexicon, etc. I tried to solve a variety of cases of active and passive sentences such as 12 English tenses, modal verbs, negative form, etc. More details and my contributions will be presented in the following sections. The source code is available at https://github.com/tqtrunghnvn/ActiveAndPassive.
Optimization of Convolutional Neural Network Using the Linearly Decreasing Weight Particle Swarm Optimization
Authors: T. Serizawa, H. Fujita
Date: 2020-01-16 00:00:00
Tasks: Hyperparameter Optimization

Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. Various forms of models have been proposed and im-proved for learning at CNN. When learning with CNN, it is necessary to determine the optimal hyperparameters. However, the number of hyperparameters is so large that it is difficult to do it manually, so much research has been done on automation. A method that uses metaheuristic algorithms is attracting attention in research on hyperparameter optimization. Metaheuristic algorithms are naturally inspired and include evolution strategies, genetic algorithms, antcolony optimization and particle swarm optimization. In particular, particle swarm optimization converges faster than genetic algorithms, and various models have been proposed. In this paper, we pro-pose CNN hyperparameter optimization with linearly decreasing weight particle swarm optimization (LDWPSO). In the experiment, the MNIST data set and CIFAR-10 data set, which are often used as benchmark data sets, are used. By opti-mizing CNN hyperparameters with LDWPSO, learning the MNIST and CIFAR-10 datasets, we compare the accuracy with a standard CNN based on LeNet-5. As a result, when using the MNIST dataset, the baseline CNN is 94.02% at the 5th epoch, compared to 98.95% for LDWPSO CNN, which improves accuracy. When using the CIFAR-10 dataset, the Baseline CNN is 28.07% at the 10th epoch, compared to 69.37% for the LDWPSO CNN, which greatly improves accuracy. This paper is presented at the 36th Annual Conference of the Japanese Society for Artificial In-telligence. The final version is available at the following URL: https://doi.org/10.11517/pjsai.JSAI2022.0_2S4IS2b03
Human-like Time Series Summaries via Trend Utility Estimation
Authors: Pegah Jandaghi, Jay Pujara
Date: 2020-01-16 00:00:00
Tasks: Time Series, Time Series Analysis

In many scenarios, humans prefer a text-based representation of quantitative data over numerical, tabular, or graphical representations. The attractiveness of textual summaries for complex data has inspired research on data-to-text systems. While there are several data-to-text tools for time series, few of them try to mimic how humans summarize for time series. In this paper, we propose a model to create human-like text descriptions for time series. Our system finds patterns in time series data and ranks these patterns based on empirical observations of human behavior using utility estimation. Our proposed utility estimation model is a Bayesian network capturing interdependencies between different patterns. We describe the learning steps for this network and introduce baselines along with their performance for each step. The output of our system is a natural language description of time series that attempts to match a human's summary of the same data.
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
Authors: Li Tao, Xueting Wang, Toshihiko Yamasaki
Date: 2020-01-16 00:00:00
Tasks: Action Recognition, Optical Flow Estimation

Recently, 3D convolutional networks yield good performance in action recognition. However, optical flow stream is still needed to ensure better performance, the cost of which is very high. In this paper, we propose a fast but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets. By replacing traditional stacked RGB frames with residual ones, 20.5% and 12.5% points improvements over top-1 accuracy can be achieved on the UCF101 and HMDB51 datasets when trained from scratch. Because residual frames contain little information of object appearance, we further use a 2D convolutional network to extract appearance features and combine them with the results from residual frames to form a two-path solution. In three benchmark datasets, our two-path solution achieved better or comparable performances than those using additional optical flow methods, especially outperformed the state-of-the-art models on Mini-kinetics dataset. Further analysis indicates that better motion features can be extracted using residual frames with 3D ConvNets, and our residual-frame-input path is a good supplement for existing RGB-frame-input models.
LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices
Authors: Hongwei Xie, Jiafang Wang, Baitao Shao, Jian Gu, Mingyang Li
Date: 2020-01-16 00:00:00
Tasks: 3D Part Segmentation, Gesture Recognition, Hand Detection, Hand Gesture Recognition, Hand-Gesture Recognition

Online hand gesture recognition (HGR) techniques are essential in augmented reality (AR) applications for enabling natural human-to-computer interaction and communication. In recent years, the consumer market for low-cost AR devices has been rapidly growing, while the technology maturity in this domain is still limited. Those devices are typical of low prices, limited memory, and resource-constrained computational units, which makes online HGR a challenging problem. To tackle this problem, we propose a lightweight and computationally efficient HGR framework, namely LE-HGR, to enable real-time gesture recognition on embedded devices with low computing power. We also show that the proposed method is of high accuracy and robustness, which is able to reach high-end performance in a variety of complicated interaction environments. To achieve our goal, we first propose a cascaded multi-task convolutional neural network (CNN) to simultaneously predict probabilities of hand detection and regress hand keypoint locations online. We show that, with the proposed cascaded architecture design, false-positive estimates can be largely eliminated. Additionally, an associated mapping approach is introduced to track the hand trace via the predicted locations, which addresses the interference of multi-handedness. Subsequently, we propose a trace sequence neural network (TraceSeqNN) to recognize the hand gesture by exploiting the motion features of the tracked trace. Finally, we provide a variety of experimental results to show that the proposed framework is able to achieve state-of-the-art accuracy with significantly reduced computational cost, which are the key properties for enabling real-time applications in low-cost commercial devices such as mobile devices and AR/VR headsets.
Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering
Authors: Dezhao Wang, Sifeng Xia, Wenhan Yang, Jiaying Liu
Date: 2020-01-16 00:00:00

In this paper, we aim to address issues of (1) joint spatial-temporal modeling and (2) side information injection for deep-learning based in-loop filter. For (1), we design a deep network with both progressive rethinking and collaborative learning mechanisms to improve quality of the reconstructed intra-frames and inter-frames, respectively. For intra coding, a Progressive Rethinking Network (PRN) is designed to simulate the human decision mechanism for effective spatial modeling. Our designed block introduces an additional inter-block connection to bypass a high-dimensional informative feature before the bottleneck module across blocks to review the complete past memorized experiences and rethinks progressively. For inter coding, the current reconstructed frame interacts with reference frames (peak quality frame and the nearest adjacent frame) collaboratively at the feature level. For (2), we extract both intra-frame and inter-frame side information for better context modeling. A coarse-to-fine partition map based on HEVC partition trees is built as the intra-frame side information. Furthermore, the warped features of the reference frames are offered as the inter-frame side information. Our PRN with intra-frame side information provides 9.0% BD-rate reduction on average compared to HEVC baseline under All-intra (AI) configuration. While under Low-Delay B (LDB), Low-Delay P (LDP) and Random Access (RA) configuration, our PRN with inter-frame side information provides 9.0%, 10.6% and 8.0% BD-rate reduction on average respectively. Our project webpage is https://dezhao-wang.github.io/PRN-v2/.
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
Authors: Jesse Haviland, Feras Dayoub, Peter Corke
Date: 2020-01-16 00:00:00
Tasks: Object, Robotic Grasping, valid

This paper considers the final approach phase of visual-closed-loop grasping where the RGB-D camera is no longer able to provide valid depth information. Many current robotic grasping controllers are not closed-loop and therefore fail for moving objects. Closed-loop grasp controllers based on RGB-D imagery can track a moving object, but fail when the sensor's minimum object distance is violated just before grasping. To overcome this we propose the use of image-based visual servoing (IBVS) to guide the robot to the object-relative grasp pose using camera RGB information. IBVS robustly moves the camera to a goal pose defined implicitly in terms of an image-plane feature configuration. In this work, the goal image feature coordinates are predicted from RGB-D data to enable RGB-only tracking once depth data becomes unavailable -- this enables more reliable grasping of previously unseen moving objects. Experimental results are provided.
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
Authors: Xiaoxiao Li, Yufeng Gu, Nicha Dvornek, Lawrence Staib, Pamela Ventola, James S. Duncan
Date: 2020-01-16 00:00:00
Tasks: Domain Adaptation, Federated Learning, Model Optimization, Privacy Preserving

Deep learning models have shown their advantage in many different tasks, including neuroimage analysis. However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. The time and cost for acquisition and annotation in assembling, for example, large fMRI datasets make it difficult to acquire large numbers at a single site. However, due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data by transmitting the global model to local entities, training the model locally, and then averaging the gradients or weights in the global model. However, some studies suggest that private information can be recovered from the model gradients or weights. In this work, we address the problem of multi-site fMRI classification with a privacy-preserving strategy. To solve the problem, we propose a federated learning approach, where a decentralized iterative optimization algorithm is implemented and shared local model weights are altered by a randomization mechanism. Considering the systemic differences of fMRI distributions from different sites, we further propose two domain adaptation methods in this federated learning formulation. We investigate various practical aspects of federated model optimization and compare federated learning with alternative training strategies. Overall, our results demonstrate that it is promising to utilize multi-site data without data sharing to boost neuroimage analysis performance and find reliable disease-related biomarkers. Our proposed pipeline can be generalized to other privacy-sensitive medical data analysis problems.
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
Authors: Saeed Amirgholipour, Xiangjian He, Wenjing Jia, Dadong Wang, Lei Liu
Date: 2020-01-16 00:00:00
Tasks: Crowd Counting, Decoder

Crowd counting, i.e., estimating the number of people in a crowded area, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast scale variations in crowd density within the interested area, and severe occlusion among the crowd. In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract different scale features, focus on the relevant information, and suppress the misleading ones. We also address the variation of crowdedness levels among different images with an exclusive Density-Aware Decoder (DAD). For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowded density maps as spatial attention. Meanwhile, we employ two losses to create a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state of the arts.
MIME: Mutual Information Minimisation Exploration
Authors: Haitao Xu, Brendan McCane, Lech Szymanski, Craig Atkinson
Date: 2020-01-16 00:00:00
Tasks: Montezuma's Revenge, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn. We propose a counter-intuitive solution that we call Mutual Information Minimising Exploration (MIME) where an agent learns a latent representation of the environment without trying to predict the future states. We show that our agent performs significantly better over sharp transition boundaries while matching the performance of surprisal driven agents elsewhere. In particular, we show state-of-the-art performance on difficult learning games such as Gravitar, Montezuma's Revenge and Doom.
Self-supervised visual feature learning with curriculum
Authors: Vishal Keshav, Fabien Delattre
Date: 2020-01-16 00:00:00
Tasks: Self-Supervised Learning

Self-supervised learning techniques have shown their abilities to learn meaningful feature representation. This is made possible by training a model on pretext tasks that only requires to find correlations between inputs or parts of inputs. However, such pretext tasks need to be carefully hand selected to avoid low level signals that could make those pretext tasks trivial. Moreover, removing those shortcuts often leads to the loss of some semantically valuable information. We show that it directly impacts the speed of learning of the downstream task. In this paper we took inspiration from curriculum learning to progressively remove low level signals and show that it significantly increase the speed of convergence of the downstream task.
Digital (co)homology modules and digital Pontryagin algebras
Authors: Dae-Woong Lee
Date: 2011-09-18 00:00:00

In the current study, we explore digital homology and cohomology modules, and investigate their fundamental properties on pointed digital images. We also examine pointed digital Hopf spaces and base point preserving digital Hopf functions between the pointed digital Hopf spaces with suitable digital multiplications, and explore the digital primitive homology and cohomology classes, the digital Pontryagin algebras and coalgebras on the digital Hopf spaces as digital images.
Cluster-based Zero-shot learning for multivariate data
Authors: Toshitaka Hayashi, Hamido Fujita
Date: 2020-01-16 00:00:00
Tasks: Binary Classification, Clustering, Zero-Shot Learning

Supervised learning requires a sufficient training dataset which includes all label. However, there are cases that some class is not in the training data. Zero-Shot Learning (ZSL) is the task of predicting class that is not in the training data(target class). The existing ZSL method is done for image data. However, the zero-shot problem should happen to every data type. Hence, considering ZSL for other data types is required. In this paper, we propose the cluster-based ZSL method, which is a baseline method for multivariate binary classification problems. The proposed method is based on the assumption that if data is far from training data, the data is considered as target class. In training, clustering is done for training data. In prediction, the data is determined belonging to a cluster or not. If data does not belong to a cluster, the data is predicted as target class. The proposed method is evaluated and demonstrated using the KEEL dataset. This paper has been published in the Journal of Ambient Intelligence and Humanized Computing. The final version is available at the following URL: https://link.springer.com/article/10.1007/s12652-020-02268-5
Estimating Aggregate Properties In Relational Networks With Unobserved Data
Authors: Varun Embar, Sriram Srinivasan, Lise Getoor
Date: 2020-01-16 00:00:00
Tasks: Relational Reasoning

Aggregate network properties such as cluster cohesion and the number of bridge nodes can be used to glean insights about a network's community structure, spread of influence and the resilience of the network to faults. Efficiently computing network properties when the network is fully observed has received significant attention (Wasserman and Faust 1994; Cook and Holder 2006), however the problem of computing aggregate network properties when there is missing data attributes has received little attention. Computing these properties for networks with missing attributes involves performing inference over the network. Statistical relational learning (SRL) and graph neural networks (GNNs) are two classes of machine learning approaches well suited for inferring missing attributes in a graph. In this paper, we study the effectiveness of these approaches in estimating aggregate properties on networks with missing attributes. We compare two SRL approaches and three GNNs. For these approaches we estimate these properties using point estimates such as MAP and mean. For SRL-based approaches that can infer a joint distribution over the missing attributes, we also estimate these properties as an expectation over the distribution. To compute the expectation tractably for probabilistic soft logic, one of the SRL approaches that we study, we introduce a novel sampling framework. In the experimental evaluation, using three benchmark datasets, we show that SRL-based approaches tend to outperform GNN-based approaches both in computing aggregate properties and predictive accuracy. Specifically, we show that estimating the aggregate properties as an expectation over the joint distribution outperforms point estimates.
Delving Deeper into the Decoder for Video Captioning
Authors: Haoran Chen, Jianmin Li, Xiaolin Hu
Date: 2020-01-16 00:00:00
Tasks: Decoder, Sentence, Video Captioning, Video Description

Video captioning is an advanced multi-modal task which aims to describe a video clip using a natural language sentence. The encoder-decoder framework is the most popular paradigm for this task in recent years. However, there exist some problems in the decoder of a video captioning model. We make a thorough investigation into the decoder and adopt three techniques to improve the performance of the model. First of all, a combination of variational dropout and layer normalization is embedded into a recurrent unit to alleviate the problem of overfitting. Secondly, a new online method is proposed to evaluate the performance of a model on a validation set so as to select the best checkpoint for testing. Finally, a new training strategy called professional learning is proposed which uses the strengths of a captioning model and bypasses its weaknesses. It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 18% on MSVD and 3.5% on MSR-VTT compared with the previous state-of-the-art models.