pwc-archive/papers-with-abstracts huggingface dataset:
ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network
Authors: Liu Ying, Heng Fan, Fuchuan Ni, Jinhai Xiang
Date: 2019-10-25 00:00:00
Tasks: Attribute, Classification, Decoder, General Classification, Generative Adversarial Network

Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging in generating high-quality images with accurate attribute transformation. Attacking these problems, the work proposes a novel selective attribute editing model based on classification adversarial network (referred to as ClsGAN) that shows good balance between attribute transfer accuracy and photo-realistic images. Considering that the editing images are prone to be affected by original attribute due to skip-connection in encoder-decoder structure, an upper convolution residual network (referred to as Tr-resnet) is presented to selectively extract information from the source image and target label. In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images. Experimental results on CelebA demonstrate that our ClsGAN performs favorably against state-of-the-art approaches in image quality and transfer accuracy. Moreover, ablation studies are also designed to verify the great performance of Tr-resnet and Atta-cls.
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
Authors: Tianrui Liu, Jun-Jie Huang, Tianhong Dai, Guangyu Ren, Tania Stathaki
Date: 2019-10-25 00:00:00
Tasks: Pedestrian Detection

Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large variant on sizes and with occlusions remains a challenging problem. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and a concatenation layer which perform feature dimension squeezing, feature elements manipulation and convolutional features combination from multiple CNN layers, respectively. We proposed two different gate models which can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting those small-size and occluded pedestrians.
Self-supervised Moving Vehicle Tracking with Stereo Sound
Authors: Chuang Gan, Hang Zhao, Peihao Chen, David Cox, Antonio Torralba
Date: 2019-10-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object Localization, vehicle detection, Visual Localization

Humans are able to localize objects in the environment using both visual and auditory cues, integrating information from multiple modalities into a common reference frame. We introduce a system that can leverage unlabeled audio-visual data to learn to localize objects (moving vehicles) in a visual reference frame, purely using stereo sound at inference time. Since it is labor-intensive to manually annotate the correspondences between audio and object bounding boxes, we achieve this goal by using the co-occurrence of visual and audio streams in unlabeled videos as a form of self-supervision, without resorting to the collection of ground-truth annotations. In particular, we propose a framework that consists of a vision "teacher" network and a stereo-sound "student" network. During training, knowledge embodied in a well-established visual vehicle detection model is transferred to the audio domain using unlabeled videos as a bridge. At test time, the stereo-sound student network can work independently to perform object localization us-ing just stereo audio and camera meta-data, without any visual input. Experimental results on a newly collected Au-ditory Vehicle Tracking dataset verify that our proposed approach outperforms several baseline approaches. We also demonstrate that our cross-modal auditory localization approach can assist in the visual localization of moving vehicles under poor lighting conditions.
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Authors: Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels, Martin Jaggi, François Fleuret
Date: 2019-10-25 00:00:00
Proceeding: ICML 2020 1
Tasks: Benchmarking

The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under near-optimal problem-specific hyperparameters, and finding these settings may be prohibitively costly for practitioners. In this work, we argue that a fair assessment of optimizers' performance must take the computational cost of hyperparameter tuning into account, i.e., how easy it is to find good hyperparameter configurations using an automatic hyperparameter search. Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, our results indicate that Adam is the most practical solution, particularly in low-budget scenarios.
Coalitional Games with Stochastic Characteristic Functions and Private Types
Authors: Dengji Zhao, Yiqing Huang, Liat Cohen, Tal Grinshpoun
Date: 2019-10-25 00:00:00

The research on coalitional games has focused on how to share the reward among a coalition such that players are incentivised to collaborate together. It assumes that the (deterministic or stochastic) characteristic function is known in advance. This paper studies a new setting (a task allocation problem) where the characteristic function is not known and it is controlled by some private information from the players. Hence, the challenge here is twofold: (i) incentivize players to reveal their private information truthfully, (ii) incentivize them to collaborate together. We show that existing reward distribution mechanisms or auctions cannot solve the challenge. Hence, we propose the very first mechanism for the problem from the perspective of both mechanism design and coalitional games.
Learning Mixtures of Plackett-Luce Models from Structured Partial Orders
Authors: Zhibing Zhao, Lirong Xia
Date: 2019-10-25 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: 2k, Computational Efficiency

Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of partial orders. In such cases, the parameter of the model may not be even identifiable. In this paper, we focus on three popular structures of partial orders: ranked top-$l_1$, $l_2$-way, and choice data over a subset of alternatives. We prove that when the dataset consists of combinations of ranked top-$l_1$ and $l_2$-way (or choice data over up to $l_2$ alternatives), mixture of $k$ Plackett-Luce models is not identifiable when $l_1+l_2\le 2k-1$ ($l_2$ is set to $1$ when there are no $l_2$-way orders). We also prove that under some combinations, including ranked top-$3$, ranked top-$2$ plus $2$-way, and choice data over up to $4$ alternatives, mixtures of two Plackett-Luce models are identifiable. Guided by our theoretical results, we propose efficient generalized method of moments (GMM) algorithms to learn mixtures of two Plackett-Luce models, which are proven consistent. Our experiments demonstrate the efficacy of our algorithms. Moreover, we show that when full rankings are available, learning from different marginal events (partial orders) provides tradeoffs between statistical efficiency and computational efficiency.
ALET (Automated Labeling of Equipment and Tools): A Dataset, a Baseline and a Usecase for Tool Detection in the Wild
Authors: Fatih Can Kurnaz, Burak Hocaoğlu, Mert Kaan Yılmaz, İdil Sülo, Sinan Kalkan
Date: 2019-10-25 00:00:00
Tasks: object-detection, Object Detection

Robots collaborating with humans in realistic environments will need to be able to detect the tools that can be used and manipulated. However, there is no available dataset or study that addresses this challenge in real settings. In this paper, we fill this gap by providing an extensive dataset (METU-ALET) for detecting farming, gardening, office, stonemasonry, vehicle, woodworking and workshop tools. The scenes correspond to sophisticated environments with or without humans using the tools. The scenes we consider introduce several challenges for object detection, including the small scale of the tools, their articulated nature, occlusion, inter-class invariance, etc. Moreover, we train and compare several state of the art deep object detectors (including Faster R-CNN, Cascade R-CNN, RepPoint and RetinaNet) on our dataset. We observe that the detectors have difficulty in detecting especially small-scale tools or tools that are visually similar to parts of other tools. This in turn supports the importance of our dataset and paper. With the dataset, the code and the trained models, our work provides a basis for further research into tools and their use in robotics applications.
Multi-scale Deep Neural Networks for Solving High Dimensional PDEs
Authors: Wei Cai, Zhi-Qin John Xu
Date: 2019-10-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we propose the idea of radial scaling in frequency domain and activation functions with compact support to produce a multi-scale DNN (MscaleDNN), which will have the multi-scale capability in approximating high frequency and high dimensional functions and speeding up the solution of high dimensional PDEs. Numerical results on high dimensional function fitting and solutions of high dimensional PDEs, using loss functions with either Ritz energy or least squared PDE residuals, have validated the increased power of multi-scale resolution and high frequency capturing of the proposed MscaleDNN.
Learning Task-Oriented Grasping from Human Activity Datasets
Authors: Mia Kokic, Danica Kragic, Jeannette Bohg
Date: 2019-10-25 00:00:00
Tasks: Object

We propose to leverage a real-world, human activity RGB dataset to teach a robot Task-Oriented Grasping (TOG). We develop a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We follow the insight that jointly estimating hand and object poses increases accuracy compared to estimating these quantities independently of each other. Given the trained model, we process an RGB dataset to automatically obtain the data to train a TOG model. This model takes as input an object point cloud and outputs a suitable region for task-specific grasping. Our ablation study shows that training an object pose predictor with the hand pose information (and vice versa) is better than training without this information. Furthermore, our results on a real-world dataset show the applicability and competitiveness of our method over state-of-the-art. Experiments with a robot demonstrate that our method can allow a robot to preform TOG on novel objects.
SPICE: Self-supervised Pitch Estimation
Authors: Beat Gfeller, Christian Frank, Dominik Roblek, Matt Sharifi, Marco Tagliasacchi, Mihajlo Velimirović
Date: 2019-10-25 00:00:00
Tasks: Self-Supervised Learning, Translation

We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations at the required temporal and frequency resolution is a particularly daunting task. Therefore, we propose to adopt a self-supervised learning technique, which is able to estimate pitch without any form of supervision. The key observation is that pitch shift maps to a simple translation when the audio signal is analysed through the lens of the constant-Q transform (CQT). We design a self-supervised task by feeding two shifted slices of the CQT to the same convolutional encoder, and require that the difference in the outputs is proportional to the corresponding difference in pitch. In addition, we introduce a small model head on top of the encoder, which is able to determine the confidence of the pitch estimate, so as to distinguish between voiced and unvoiced audio. Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, although it does not require access to large labeled datasets.
Attend to the Difference: Cross-Modality Person Re-identification via Contrastive Correlation
Authors: Shizhou Zhang, Yifei Yang, Peng Wang, Guoqiang Liang, Xiuwei Zhang, Yanning Zhang
Date: 2019-10-25 00:00:00
Tasks: Cross-Modality Person Re-identification, Person Re-Identification

The problem of cross-modality person re-identification has been receiving increasing attention recently, due to its practical significance. Motivated by the fact that human usually attend to the difference when they compare two similar objects, we propose a dual-path cross-modality feature learning framework which preserves intrinsic spatial strictures and attends to the difference of input cross-modality image pairs. Our framework is composed by two main components: a Dual-path Spatial-structure-preserving Common Space Network (DSCSN) and a Contrastive Correlation Network (CCN). The former embeds cross-modality images into a common 3D tensor space without losing spatial structures, while the latter extracts contrastive features by dynamically comparing input image pairs. Note that the representations generated for the input RGB and Infrared images are mutually dependant to each other. We conduct extensive experiments on two public available RGB-IR ReID datasets, SYSU-MM01 and RegDB, and our proposed method outperforms state-of-the-art algorithms by a large margin with both full and simplified evaluation modes.
Reducing Domain Gap by Reducing Style Bias
Authors: Hyeonseob Nam, Hyunjae Lee, Jongchan Park, Wonjun Yoon, Donggeun Yoo
Date: 2019-10-25 00:00:00
Proceeding: CVPR 2021 1
Tasks: Domain Adaptation, Domain Generalization, Image to sketch recognition, Inductive Bias, Semi-supervised Domain Adaptation, Single-Source Domain Generalization, Unsupervised Domain Adaptation

Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to domain changes, rather than contents (i.e. shapes). Inspired by this, we propose to reduce the intrinsic style bias of CNNs to close the gap between domains. Our Style-Agnostic Networks (SagNets) disentangle style encodings from class categories to prevent style biased predictions and focus more on the contents. Extensive experiments show that our method effectively reduces the style bias and makes the model more robust under domain shift. It achieves remarkable performance improvements in a wide range of cross-domain tasks including domain generalization, unsupervised domain adaptation, and semi-supervised domain adaptation on multiple datasets.
Channel adversarial training for speaker verification and diarization
Authors: Chau Luu, Peter Bell, Steve Renals
Date: 2019-10-25 00:00:00
Tasks: Speaker Verification

Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong. We propose a training strategy which aims to produce features that are invariant at the granularity of the recording or channel, a finer grained objective than dataset- or environment-invariance. By training an adversary to predict whether pairs of same-speaker embeddings belong to the same recording in a Siamese fashion, learned features are discouraged from utilizing channel information that may be speaker discriminative during training. Experiments for verification on VoxCeleb and diarization and verification on CALLHOME show promising improvements over a strong baseline in addition to outperforming a dataset-adversarial model. The VoxCeleb model in particular performs well, achieving a $4\%$ relative improvement in EER over a Kaldi baseline, while using a similar architecture and less training data.
Mixing realities for sketch retrieval in Virtual Reality
Authors: Daniele Giunchi, Stuart James, Donald Degraen, Anthony Steed
Date: 2019-10-25 00:00:00
Tasks: Retrieval

Drawing tools for Virtual Reality (VR) enable users to model 3D designs from within the virtual environment itself. These tools employ sketching and sculpting techniques known from desktop-based interfaces and apply them to hand-based controller interaction. While these techniques allow for mid-air sketching of basic shapes, it remains difficult for users to create detailed and comprehensive 3D models. In our work, we focus on supporting the user in designing the virtual environment around them by enhancing sketch-based interfaces with a supporting system for interactive model retrieval. Through sketching, an immersed user can query a database containing detailed 3D models and replace them into the virtual environment. To understand supportive sketching within a virtual environment, we compare different methods of sketch interaction, i.e., 3D mid-air sketching, 2D sketching on a virtual tablet, 2D sketching on a fixed virtual whiteboard, and 2D sketching on a real tablet. %using a 2D physical tablet, a 2D virtual tablet, a 2D virtual whiteboard, and 3D mid-air sketching. Our results show that 3D mid-air sketching is considered to be a more intuitive method to search a collection of models while the addition of physical devices creates confusion due to the complications of their inclusion within a virtual environment. While we pose our work as a retrieval problem for 3D models of chairs, our results can be extrapolated to other sketching tasks for virtual environments.
An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models
Authors: Michael J. Klaiber, Sebastian Vogel, Axel Acosta, Robert Korn, Leonardo Ecco, Kristine Back, Andre Guntoro, Ingo Feldner
Date: 2019-10-25 00:00:00

End-to-end performance estimation and measurement of deep neural network (DNN) systems become more important with increasing complexity of DNN systems consisting of hardware and software components. The methodology proposed in this paper aims at a reduced turn-around time for evaluating different design choices of hardware and software components of DNN systems. This reduction is achieved by moving the performance estimation from the implementation phase to the concept phase by employing virtual hardware models instead of gathering measurement results from physical prototypes. Deep learning compilers introduce hardware-specific transformations and are, therefore, considered a part of the design flow of virtual system models to extract end-to-end performance estimations. To validate the run-time accuracy of the proposed methodology, a system processing the DilatedVGG DNN is realized both as virtual system model and as hardware implementation. The results show that up to 92 % accuracy can be reached in predicting the processing time of the DNN inference.
Learning to Localize Temporal Events in Large-scale Video Data
Authors: Mikel Bober-Irizar, Miha Skalic, David Austin
Date: 2019-10-25 00:00:00
Tasks: Temporal Localization, Video Recognition

We address temporal localization of events in large-scale video data, in the context of the Youtube-8M Segments dataset. This emerging field within video recognition can enable applications to identify the precise time a specified event occurs in a video, which has broad implications for video search. To address this we present two separate approaches: (1) a gradient boosted decision tree model on a crafted dataset and (2) a combination of deep learning models based on frame-level data, video-level data, and a localization model. The combinations of these two approaches achieved 5th place in the 3rd Youtube-8M video recognition challenge.
Towards Robust and Stable Deep Learning Algorithms for Forward Backward Stochastic Differential Equations
Authors: Batuhan Güler, Alexis Laignelet, Panos Parpas
Date: 2019-10-25 00:00:00
Tasks: Deep Learning, Management

Applications in quantitative finance such as optimal trade execution, risk management of options, and optimal asset allocation involve the solution of high dimensional and nonlinear Partial Differential Equations (PDEs). The connection between PDEs and systems of Forward-Backward Stochastic Differential Equations (FBSDEs) enables the use of advanced simulation techniques to be applied even in the high dimensional setting. Unfortunately, when the underlying application contains nonlinear terms, then classical methods both for simulation and numerical methods for PDEs suffer from the curse of dimensionality. Inspired by the success of deep learning, several researchers have recently proposed to address the solution of FBSDEs using deep learning. We discuss the dynamical systems point of view of deep learning and compare several architectures in terms of stability, generalization, and robustness. In order to speed up the computations, we propose to use a multilevel discretization technique. Our preliminary results suggest that the multilevel discretization method improves solutions times by an order of magnitude compared to existing methods without sacrificing stability or robustness.
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Authors: Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei zhang, Huajun Chen
Date: 2019-10-25 00:00:00
Tasks: Event Detection, Event Extraction, Few-Shot Learning, Meta-Learning, Sentence

Event detection (ED), a sub-task of event extraction, involves identifying triggers and categorizing event mentions. Existing methods primarily rely upon supervised learning and require large-scale labeled event datasets which are unfortunately not readily available in many real-life applications. In this paper, we consider and reformulate the ED task with limited labeled data as a Few-Shot Learning problem. We propose a Dynamic-Memory-Based Prototypical Network (DMB-PN), which exploits Dynamic Memory Network (DMN) to not only learn better prototypes for event types, but also produce more robust sentence encodings for event mentions. Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs. The experiments show that DMB-PN not only deals with sample scarcity better than a series of baseline models but also performs more robustly when the variety of event types is relatively large and the instance quantity is extremely small.