pwc-archive/papers-with-abstracts huggingface dataset:
Generalizable Pedestrian Detection: The Elephant In The Room
Authors: Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram, Ling Shao
Date: 2020-03-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Autonomous Driving, Pedestrian Detection

Pedestrian detection is used in many vision based applications ranging from video surveillance to autonomous driving. Despite achieving high performance, it is still largely unknown how well existing detectors generalize to unseen data. This is important because a practical detector should be ready to use in various scenarios in applications. To this end, we conduct a comprehensive study in this paper, using a general principle of direct cross-dataset evaluation. Through this study, we find that existing state-of-the-art pedestrian detectors, though perform quite well when trained and tested on the same dataset, generalize poorly in cross dataset evaluation. We demonstrate that there are two reasons for this trend. Firstly, their designs (e.g. anchor settings) may be biased towards popular benchmarks in the traditional single-dataset training and test pipeline, but as a result largely limit their generalization capability. Secondly, the training source is generally not dense in pedestrians and diverse in scenarios. Under direct cross-dataset evaluation, surprisingly, we find that a general purpose object detector, without pedestrian-tailored adaptation in design, generalizes much better compared to existing state-of-the-art pedestrian detectors. Furthermore, we illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection. Accordingly, we propose a progressive training pipeline and find that it works well for autonomous-driving oriented pedestrian detection. Consequently, the study conducted in this paper suggests that more emphasis should be put on cross-dataset evaluation for the future design of generalizable pedestrian detectors. Code and models can be accessed at https://github.com/hasanirtiza/Pedestron.
High-Resolution Daytime Translation Without Domain Labels
Authors: Ivan Anokhin, Pavel Solovev, Denis Korzhenkov, Alexey Kharlamov, Taras Khakhulin, Alexey Silvestrov, Sergey Nikolenko, Victor Lempitsky, Gleb Sterkin
Date: 2020-03-19 00:00:00
Proceeding: CVPR 2020 6
Tasks: Image Manipulation, Image Super-Resolution, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Style Transfer, Translation, Unsupervised Image-To-Image Translation, Vocal Bursts Intensity Prediction

Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-resolution daytime translation (HiDT) model for this task. HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. The model demonstrates competitive results in terms of both commonly used GAN metrics and human evaluation. Importantly, this good performance comes as a result of training on a dataset of still landscape images with no daytime labels available. Our results are available at https://saic-mdal.github.io/HiDT/.
Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection
Authors: Zongxian Li, Qixiang Ye, Chong Zhang, Jingjing Liu, Shijian Lu, Yonghong Tian
Date: 2020-03-19 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) has achieved unprecedented success in improving the cross-domain robustness of object detection models. However, existing UDA methods largely ignore the instantaneous data distribution during model learning, which could deteriorate the feature representation given large domain shift. In this work, we propose a Self-Guided Adaptation (SGA) model, target at aligning feature representation and transferring object detection models across domains while considering the instantaneous alignment difficulty. The core of SGA is to calculate "hardness" factors for sample pairs indicating domain distance in a kernel space. With the hardness factor, the proposed SGA adaptively indicates the importance of samples and assigns them different constrains. Indicated by hardness factors, Self-Guided Progressive Sampling (SPS) is implemented in an "easy-to-hard" way during model adaptation. Using multi-stage convolutional features, SGA is further aggregated to fully align hierarchical representations of detection models. Extensive experiments on commonly used benchmarks show that SGA improves the state-of-the-art methods with significant margins, while demonstrating the effectiveness on large domain shift.
Exemplar Normalization for Learning Deep Representation
Authors: Ruimao Zhang, Zhanglin Peng, Lingyun Wu, Zhen Li, Ping Luo
Date: 2020-03-19 00:00:00
Proceeding: CVPR 2020 6
Tasks: Semantic Segmentation

Normalization techniques are important in different advanced neural networks and different tasks. This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network. EN significantly improves flexibility of the recently proposed switchable normalization (SN), which solves a static L2N problem by linearly combining several normalizers in each normalization layer (the combination is the same for all samples). Instead of directly employing a multi-layer perceptron (MLP) to learn data-dependent parameters as conditional batch normalization (cBN) did, the internal architecture of EN is carefully designed to stabilize its optimization, leading to many appealing benefits. (1) EN enables different convolutional layers, image samples, categories, benchmarks, and tasks to use different normalization methods, shedding light on analyzing them in a holistic view. (2) EN is effective for various network architectures and tasks. (3) It could replace any normalization layers in a deep network and still produce stable model training. Extensive experiments demonstrate the effectiveness of EN in a wide spectrum of tasks including image recognition, noisy label learning, and semantic segmentation. For example, by replacing BN in the ordinary ResNet50, improvement produced by EN is 300% more than that of SN on both ImageNet and the noisy WebVision dataset.
Decentralized MCTS via Learned Teammate Models
Authors: Aleksander Czechowski, Frans A. Oliehoek
Date: 2020-03-19 00:00:00

Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness. A key difficulty of such approach lies in making accurate predictions about the decisions of other agents. In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of teammates learned from previous episodic runs. By only allowing one agent to adapt its models at a time, under the assumption of ideal policy approximation, successive iterations of our method are guaranteed to improve joint policies, and eventually lead to convergence to a Nash equilibrium. We test the efficiency of the algorithm by performing experiments in several scenarios of the spatial task allocation environment introduced in [Claes et al., 2015]. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators which exploit the spatial features of the problem, and that the proposed algorithm improves over the baseline planning performance for particularly challenging domain configurations.
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
Authors: Yi Liu, James J. Q. Yu, Jiawen Kang, Dusit Niyato, Shuyu Zhang
Date: 2020-03-19 00:00:00
Tasks: Clustering, Federated Learning, Prediction, Privacy Preserving, Traffic Prediction

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. However, these datasets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a trade-off between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning and propose a Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a Federated Averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a Joint Announcement Protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for traffic flow prediction by grouping the organizations into clusters before applying FedGRU algorithm. Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.
Faster SVM Training via Conjugate SMO
Authors: Alberto Torres-Barrán, Carlos Alaíz, José R. Dorronsoro
Date: 2020-03-19 00:00:00

We propose an improved version of the SMO algorithm for training classification and regression SVMs, based on a Conjugate Descent procedure. This new approach only involves a modest increase on the computational cost of each iteration but, in turn, usually results in a substantial decrease in the number of iterations required to converge to a given precision. Besides, we prove convergence of the iterates of this new Conjugate SMO as well as a linear rate when the kernel matrix is positive definite. We have implemented Conjugate SMO within the LIBSVM library and show experimentally that it is faster for many hyper-parameter configurations, being often a better option than second order SMO when performing a grid-search for SVM tuning.
Giving Commands to a Self-driving Car: A Multimodal Reasoner for Visual Grounding
Authors: Thierry Deruyttere, Guillem Collell, Marie-Francine Moens
Date: 2020-03-19 00:00:00
Tasks: Object, Referring Expression Comprehension, Region Proposal, Visual Grounding

We propose a new spatial memory module and a spatial reasoner for the Visual Grounding (VG) task. The goal of this task is to find a certain object in an image based on a given textual query. Our work focuses on integrating the regions of a Region Proposal Network (RPN) into a new multi-step reasoning model which we have named a Multimodal Spatial Region Reasoner (MSRR). The introduced model uses the object regions from an RPN as initialization of a 2D spatial memory and then implements a multi-step reasoning process scoring each region according to the query, hence why we call it a multimodal reasoner. We evaluate this new model on challenging datasets and our experiments show that our model that jointly reasons over the object regions of the image and words of the query largely improves accuracy compared to current state-of-the-art models.
Leveraging Frequency Analysis for Deep Fake Image Recognition
Authors: Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz
Date: 2020-03-19 00:00:00
Proceeding: ICML 2020 1
Tasks: Image Forensics

Deep neural networks can generate images that are astonishingly realistic, so much so that it is often hard for humans to distinguish them from actual photos. These achievements have been largely made possible by Generative Adversarial Networks (GANs). While deep fake images have been thoroughly investigated in the image domain - a classical approach from the area of image forensics - an analysis in the frequency domain has been missing so far. In this paper, we address this shortcoming and our results reveal that in frequency space, GAN-generated images exhibit severe artifacts that can be easily identified. We perform a comprehensive analysis, showing that these artifacts are consistent across different neural network architectures, data sets, and resolutions. In a further investigation, we demonstrate that these artifacts are caused by upsampling operations found in all current GAN architectures, indicating a structural and fundamental problem in the way images are generated via GANs. Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.
Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment
Authors: Rui Xiang, Rongjie Lai, Hongkai Zhao
Date: 2020-03-19 00:00:00
Proceeding: CVPR 2020 6

In this work, we introduce a novel local pairwise descriptor and then develop a simple, effective iterative method to solve the resulting quadratic assignment through sparsity control for shape correspondence between two approximate isometric surfaces. Our pairwise descriptor is based on the stiffness and mass matrix of finite element approximation of the Laplace-Beltrami differential operator, which is local in space, sparse to represent, and extremely easy to compute while containing global information. It allows us to deal with open surfaces, partial matching, and topological perturbations robustly. To solve the resulting quadratic assignment problem efficiently, the two key ideas of our iterative algorithm are: 1) select pairs with good (approximate) correspondence as anchor points, 2) solve a regularized quadratic assignment problem only in the neighborhood of selected anchor points through sparsity control. These two ingredients can improve and increase the number of anchor points quickly while reducing the computation cost in each quadratic assignment iteration significantly. With enough high-quality anchor points, one may use various pointwise global features with reference to these anchor points to further improve the dense shape correspondence. We use various experiments to show the efficiency, quality, and versatility of our method on large data sets, patches, and point clouds (without global meshes).
Survey of Personalization Techniques for Federated Learning
Authors: Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant
Date: 2020-03-19 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Survey

Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.
Semi-analytic approximate stability selection for correlated data in generalized linear models
Authors: Takashi Takahashi, Yoshiyuki Kabashima
Date: 2020-03-19 00:00:00
Tasks: Variable Selection

We consider the variable selection problem of generalized linear models (GLMs). Stability selection (SS) is a promising method proposed for solving this problem. Although SS provides practical variable selection criteria, it is computationally demanding because it needs to fit GLMs to many re-sampled datasets. We propose a novel approximate inference algorithm that can conduct SS without the repeated fitting. The algorithm is based on the replica method of statistical mechanics and vector approximate message passing of information theory. For datasets characterized by rotation-invariant matrix ensembles, we derive state evolution equations that macroscopically describe the dynamics of the proposed algorithm. We also show that their fixed points are consistent with the replica symmetric solution obtained by the replica method. Numerical experiments indicate that the algorithm exhibits fast convergence and high approximation accuracy for both synthetic and real-world data.
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images
Authors: Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski
Date: 2020-03-19 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network

One of the most challenges in medical imaging is the lack of data. It is proven that classical data augmentation methods are useful but still limited due to the huge variation in images. Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions. In this paper, we propose a deep convolutional conditional generative adversarial network to generate MIP positron emission tomography image (PET) which is a 2D image that represents a 3D volume for fast interpretation, according to different lesions or non lesion (normal). The advantage of our proposed method consists of one model that is capable of generating different classes of lesions trained on a small sample size for each class of lesion, and showing a very promising results. In addition, we show that a walk through a latent space can be used as a tool to evaluate the images generated.
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units
Authors: Guangli Li, Lei Liu, Xueying Wang, Xiu Ma, Xiaobing Feng
Date: 2020-03-19 00:00:00
Tasks: image-classification, Image Classification, Quantization

Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quantized Winograd convolution algorithm, called LANCE, which combines the advantages of fast convolution and quantization techniques. By embedding linear quantization operations into the Winograd-domain, the fast convolution can be performed efficiently under low-precision computation on graphics processing units. We test neural network models with LANCE on representative image classification datasets, including SVHN, CIFAR, and ImageNet. The experimental results show that our 8-bit quantized Winograd convolution improves the performance by up to 2.40x over the full-precision convolution with trivial accuracy loss.
Detecting Deepfakes with Metric Learning
Authors: Akash Kumar, Arnav Bhavsar
Date: 2020-03-19 00:00:00
Tasks: Data Compression, Face Swapping, General Classification, Metric Learning, Triplet

With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenario and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.
Photo-Realistic Video Prediction on Natural Videos of Largely Changing Frames
Authors: Osamu Shouno
Date: 2020-03-19 00:00:00
Tasks: Prediction, Video Prediction

Recent advances in deep learning have significantly improved performance of video prediction. However, state-of-the-art methods still suffer from blurriness and distortions in their future predictions, especially when there are large motions between frames. To address these issues, we propose a deep residual network with the hierarchical architecture where each layer makes a prediction of future state at different spatial resolution, and these predictions of different layers are merged via top-down connections to generate future frames. We trained our model with adversarial and perceptual loss functions, and evaluated it on a natural video dataset captured by car-mounted cameras. Our model quantitatively outperforms state-of-the-art baselines in future frame prediction on video sequences of both largely and slightly changing frames. Furthermore, our model generates future frames with finer details and textures that are perceptually more realistic than the baselines, especially under fast camera motions.
Backdooring and Poisoning Neural Networks with Image-Scaling Attacks
Authors: Erwin Quiring, Konrad Rieck
Date: 2020-03-19 00:00:00

Backdoors and poisoning attacks are a major threat to the security of machine-learning and vision systems. Often, however, these attacks leave visible artifacts in the images that can be visually detected and weaken the efficacy of the attacks. In this paper, we propose a novel strategy for hiding backdoor and poisoning attacks. Our approach builds on a recent class of attacks against image scaling. These attacks enable manipulating images such that they change their content when scaled to a specific resolution. By combining poisoning and image-scaling attacks, we can conceal the trigger of backdoors as well as hide the overlays of clean-label poisoning. Furthermore, we consider the detection of image-scaling attacks and derive an adaptive attack. In an empirical evaluation, we demonstrate the effectiveness of our strategy. First, we show that backdoors and poisoning work equally well when combined with image-scaling attacks. Second, we demonstrate that current detection defenses against image-scaling attacks are insufficient to uncover our manipulations. Overall, our work provides a novel means for hiding traces of manipulations, being applicable to different poisoning approaches.