mtybilly/Instruct2DS huggingface dataset:
Image Synthesis From Reconfigurable Layout and Style
Authors: Tianfu Wu Wei Sun
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long- standing problem to learn generative models that are capable of synthesizing realistic and sharp images from re- configurable spatial layout (i.e., bounding boxes + class labels in an image lattice) and style (i.e., structural and appearance variations encoded by latent vectors), especially at high resolution. By reconfigurable, it means that a model can preserve the intrinsic one-to-many mapping from a given layout to multiple plausible images with different styles, and is adaptive with respect to perturbations of a layout and style latent code. In this paper, we present a layout- and style-based architecture for generative adversarial networks (termed LostGANs) that can be trained end-to-end to generate images from reconfigurable layout and style. Inspired by the vanilla StyleGAN, the proposed LostGAN consists of two new components: (i) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images, and (ii) learning object instance-specific layout-aware feature normalization (ISLA-Norm) in the generator to realize multi-object style generation. In experiments, the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. The code and pretrained models are available at https://github.com/iVMCL/LostGANs.
Attribute Manipulation Generative Adversarial Networks for Fashion Images
Authors: Joo Hwee Lim,  Jo Yew Tham,  Ashraf A. Kassim Kenan E. Ak
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Recent advances in Generative Adversarial Networks (GANs) have made it possible to conduct multi-domain image-to-image translation using a single generative network. While recent methods such as Ganimation and SaGAN are able to conduct translations on attribute-relevant regions using attention, they do not perform well when the number of attributes increases as the training of attention masks mostly rely on classification losses. To address this and other limitations, we introduce Attribute Manipulation Generative Adversarial Networks (AMGAN) for fashion images. While AMGAN's generator network uses class activation maps (CAMs) to empower its attention mechanism, it also exploits perceptual losses by assigning reference (target) images based on attribute similarities. AMGAN incorporates an additional discriminator network that focuses on attribute-relevant regions to detect unrealistic translations. Additionally, AMGAN can be controlled to perform attribute manipulations on specific regions such as the sleeve or torso regions. Experiments show that AMGAN outperforms state-of-the-art methods using traditional evaluation metrics as well as an alternative one that is based on image retrieval.
Few-Shot Unsupervised Image-to-Image Translation
Authors: Xun Huang,  Arun Mallya,  Tero Karras,  Timo Aila,  Jaakko Lehtinen,  Jan Kautz Ming-Yu Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT
Very Long Natural Scenery Image Prediction by Outpainting
Authors: Jian Dong,  Ping Liu,  Yi Yang,  Shuicheng Yan Zongxin Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between generated images and original input. The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input. To solve the two problems, we devise some innovative modules, named Skip Horizontal Connection and Recurrent Content Transfer, and integrate them into our designed encoder-decoder structure. By this design, our network can generate highly realistic outpainting prediction effectively and efficiently. Other than that, our method can generate new images with very long sizes while keeping the same style and semantic content as the given input. To test the effectiveness of the proposed architecture, we collect a new scenery dataset with diverse, complicated natural scenes. The experimental results on this dataset have demonstrated the efficacy of our proposed network.
Scaling Recurrent Models via Orthogonal Approximations in Tensor Trains
Authors: Rudrasis Chakraborty,  Yunyang Xiong,  Vikas Singh Ronak Mehta
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Modern deep networks have proven to be very effective for analyzing real world images. However, their application in medical imaging is still in its early stages, primarily due to the large size of three-dimensional images, requiring enormous convolutional or fully connected layers - if we treat an image (and not image patches) as a sample. These issues only compound when the focus moves towards longitudinal analysis of 3D image volumes through recurrent structures, and when a point estimate of model parameters is insufficient in scientific applications where a reliability measure is necessary. Using insights from differential geometry, we adapt the tensor train decomposition to construct networks with significantly fewer parameters, allowing us to train powerful recurrent networks on whole brain image volume sequences. We describe the "orthogonal" tensor train, and demonstrate its ability to express a standard network layer both theoretically and empirically. We show its ability to effectively reconstruct whole brain volumes with faster convergence and stronger confidence intervals compared to the standard tensor train decomposition. We provide code and show experiments on the ADNI dataset using image sequences to regress on a cognition related outcome.
A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents
Authors: Woojae Kim,  Heeseok Oh,  Seongmin Lee,  Sanghoon Lee Jinwoo Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: What if we could interpret the cognitive state of a user while experiencing a virtual reality (VR) and estimate the cognitive state from a visual stimulus? In this paper, we address the above question by developing an electroencephalography (EEG) driven VR cybersickness prediction model. The EEG data has been widely utilized to learn the cognitive representation of brain activity. In the first stage, to fully exploit the advantages of the EEG data, it is transformed into the multi-channel spectrogram which enables to account for the correlation of spectral and temporal coefficient. Then, a convolutional neural network (CNN) is applied to encode the cognitive representation of the EEG spectrogram. In the second stage, we train a cybersickness prediction model on the VR video sequence by designing a Recurrent Neural Network (RNN). Here, the encoded cognitive representation is transferred to the model to train the visual and cognitive features for cybersickness prediction. Through the proposed framework, it is possible to predict the cybersickness level that reflects brain activity automatically. We use 8-channels EEG data to record brain activity while more than 200 subjects experience 44 different VR contents. After rigorous training, we demonstrate that the proposed framework reliably estimates cognitive states without the EEG data. Furthermore, it achieves state-of-the-art performance comparing to existing VR cybersickness prediction models.
Learning With Unsure Data for Medical Image Diagnosis
Authors: Xinwei Sun,  Lingjing Hu,  Yizhou Wang Botong Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In image-based disease prediction, it can be hard to give certain cases a deterministic "disease/normal" label due to lack of enough information, e.g., at its early stage. We call such cases "unsure" data. Labeling such data as unsure suggests follow-up examinations so as to avoid irreversible medical accident/loss in contrast to incautious prediction. This is a common practice in clinical diagnosis, however, mostly neglected by existing methods. Learning with unsure data also interweaves with two other practical issues: (i) data imbalance issue that may incur model-bias towards the majority class, and (ii) conservative/aggressive strategy consideration, i.e., the negative (normal) samples and positive (disease) samples should NOT be treated equally \-- the former should be detected with a high precision (conservativeness) and the latter should be detected with a high recall (aggression) to avoid missing opportunity for treatment. Mixed with these issues, learning with unsure data becomes particularly challenging. In this paper, we raise "learning with unsure data" problem and formulate it as an ordinal regression and propose a unified end-to-end learning framework, which also considers the aforementioned two issues: (i) incorporate cost-sensitive parameters to alleviate the data imbalance problem, and (ii) execute the conservative and aggressive strategies by introducing two parameters in the training procedure. The benefits of learning with unsure data and validity of our models are demonstrated on the prediction of Alzheimer's Disease and lung nodules.
Recursive Cascaded Networks for Unsupervised Medical Image Registration
Authors: Yue Dong,  Eric I-Chao Chang,  Yan Xu Shengyu Zhao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design and can be built on any base network. The moving image is warped successively by each cascade and finally aligned to the fixed image; this procedure is recursive in a way that every cascade learns to perform a progressive deformation for the current warped image. The entire system is end-to-end and jointly trained in an unsupervised manner. In addition, enabled by the recursive architecture, one cascade can be iteratively applied for multiple times during testing, which approaches a better fit between each of the image pairs. We evaluate our method on 3D medical images, where deformable registration is most commonly applied. We demonstrate that recursive cascaded networks achieve consistent, significant gains and outperform state-of-the-art methods. The performance reveals an increasing trend as long as more cascades are trained, while the limit is not observed. Code is available at https://github.com/zsyzzsoft/Recursive-Cascaded-Networks.
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer
Authors: Ronak Mehta,  Hao H. Zhou,  Zhichun Huang,  Sterling C. Johnson,  Vivek Prabhakaran,  Vikas Singh Haoliang Sun
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data. We derive new flow-based generative models which we show perform well in this small sample size regime (much smaller than dataset sizes available in standard vision tasks). Our formulation, DUAL-GLOW, is based on two invertible networks and a relation network that maps the latent spaces to each other. We discuss how given the prior distribution, learning the conditional distribution of PET given the MRI image reduces to obtaining the conditional distribution between the two latent codes w.r.t. the two image types. We also extend our framework to leverage "side" information (or attributes) when available. By controlling the PET generation through "conditioning" on age, our model is also able to capture brain FDG-PET (hypometabolism) changes, as a function of age. We present experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with 826 subjects, and obtain good performance in PET image synthesis, qualitatively and quantitatively better than recent works.
Dilated Convolutional Neural Networks for Sequential Manifold-Valued Data
Authors: Rudrasis Chakraborty,  Nicholas Vogt,  Barbara B. Bendlin,  Vikas Singh Xingjian Zhen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Efforts are underway to study ways via which the power of deep neural networks can be extended to non-standard data types such as structured data (e.g., graphs) or manifold-valued data (e.g., unit vectors or special matrices). Often, sizable empirical improvements are possible when the geometry of such data spaces are incorporated into the design of the model, architecture, and algorithms. Motivated by neuroimaging applications, we study formulations where the data are   sequential manifold-valued measurements . This case is common in brain imaging, where the samples correspond to symmetric positive definite matrices or orientation distribution functions. Instead of a recurrent model which poses computational/technical issues, and inspired by recent results showing the viability of dilated convolutional models for sequence prediction, we develop a dilated convolutional neural network architecture for this task. On the technical side, we show how the modules needed in our network can be derived while explicitly taking the Riemannian manifold structure into account. We show how the operations needed can leverage known results for calculating the weighted Frechet Mean (wFM). Finally, we present scientific results for group difference analysis in Alzheimer's disease (AD) where the groups are derived using AD pathology load: here the model finds several brain fiber bundles that are related to AD even when the subjects are all still cognitively healthy.
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision
Authors: Gangming Zhao,  Yu Fei,  Ming Zhang,  Yizhou Wang,  Yizhou Yu Jingyu Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Obstacles facing accurate identification and localization of diseases in chest X-ray images lie in the lack of high-quality images and annotations. In this paper, we propose a Contrast Induced Attention Network (CIA-Net), which exploits the highly structured property of chest X-ray images and localizes diseases via contrastive learning on the aligned positive and negative samples. To force the attention module to focus only on sites of abnormalities, we also introduce a learnable alignment module to adjust all the input images, which eliminates variations of scales, angles, and displacements of X-ray images generated under bad scan conditions. We show that the use of contrastive attention and alignment module allows the model to learn rich identification and localization information using only a small amount of location annotations, resulting in state-of-the-art performance in NIH chest X-ray dataset.
Joint Acne Image Grading and Counting via Label Distribution Learning
Authors: Ni Wen,  Jie Liang,  Yu-Kun Lai,  Dongyu She,  Ming-Ming Cheng,  Jufeng Yang Xiaoping Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Accurate grading of skin disease severity plays a crucial role in precise treatment for patients. Acne vulgaris, the most common skin disease in adolescence, can be graded by evidence-based lesion counting as well as experience-based global estimation in the medical field. However, due to the appearance similarity of acne with close severity, it is challenging to count and grade acne accurately. In this paper, we address the problem of acne image analysis via Label Distribution Learning (LDL) considering the ambiguous information among acne severity. Based on the professional grading criterion, we generate two acne label distributions considering the relationship between the similar number of lesions and severity of acne, respectively. We also propose a unified framework for joint acne image grading and counting, which is optimized by the multi-task learning loss. In addition, we further build the ACNE04 dataset with annotations of acne severity and lesion number of each image for evaluation. Experiments demonstrate that our proposed framework performs favorably against state-of-the-art methods. We make the code and dataset publicly available at https://github.com/xpwu95/ldl.
An Alarm System for Segmentation Algorithm Based on Shape Model
Authors: Yingda Xia,  Dong Yang,  Alan L. Yuille,  Daguang Xu Fengze Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile, manual inspection of each case to locate the failures becomes infeasible due to the trend of large data scale and limited human resource. Therefore, we build an alarm system that will set off alerts when the segmentation result is possibly unsatisfactory, assuming no corresponding ground truth mask is provided. One plausible solution is to project the segmentation results into a low dimensional feature space; then learn classifiers/regressors to predict their qualities. Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data. The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks. During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values. Thus, the VAE is able to evaluate the quality of segmentation result on unseen data, without using ground truth. Finally, we learn a regressor in the one-dimensional feature space to predict the qualities of segmentation results. Our alarm system is evaluated on several recent state-of-art segmentation algorithms for 3D medical segmentation tasks. Compared with other standard quality assessment methods, our system consistently provides more reliable prediction on the qualities of segmentation results.
HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images
Authors: Mahdi S. Hosseini,  Corwyn Rowsell,  Konstantinos N. Plataniotis,  Savvas Damaskinos Lyndon Chan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before reviewing them. Screening for ROIs is a tedious and time-consuming visual recognition task which can be exhausting. The cognitive workload could be reduced by developing a visual aid to narrow down the visual search area by highlighting (or segmenting) regions of diagnostic relevance, enabling pathologists to spend more time diagnosing relevant ROIs. In this paper, we propose HistoSegNet, a method for semantic segmentation of histological tissue type (HTT). Using the HTT-annotated Atlas of Digital Pathology (ADP) database, we train a Convolutional Neural Network on the patch annotations, infer Gradient-Weighted Class Activation Maps, average overlapping predictions, and post-process the segmentation with a fully-connected Conditional Random Field. Our method out-performs more complicated weakly-supervised semantic segmentation methods and can generalize to other datasets without retraining.
Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Authors: Zhe Li,  Song Bai,  Chong Wang,  Xinlei Chen,  Mei Han,  Elliot Fishman,  Alan L. Yuille Yuyin Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from experienced radiologists, it is common that training data is usually partially-labeled. However, these background labels can be misleading in multi-organ segmentation since the "background" usually contains some other organs of interest. To address the background ambiguity in these partially-labeled datasets, we propose Prior-aware Neural Network (PaNN) via explicitly incorporating anatomical priors on abdominal organ sizes, guiding the training process with domain-specific knowledge. More specifically, PaNN assumes that the average organ size distributions in the abdomen should approximate their empirical distributions, a prior statistics obtained from the fully-labeled dataset. As our objective is difficult to be directly optimized using stochastic gradient descent, it is reformulated as a min-max form and optimized via the stochastic primal-dual gradient algorithm. PaNN achieves state-of-the-art performance on the MICCAI2015 challenge "Multi-Atlas Labeling Beyond the Cranial Vault", a competition on organ segmentation in the abdomen. We report an average Dice score of 84.97%, surpassing the prior art by a large margin of 3.27%. Code and models will be made publicly available.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
Authors: Zhigang Song,  Zhuo Sun,  Calvin Ku,  Zhe Yang,  Cancheng Liu,  Shuhao Wang,  Jianpeng Ma,  Wei Xu Gang Xu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labels are further assigned to the corresponding pixels, producing the approximate pixel-level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance-level classification and pixel-level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis.
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples With Applications to Neuroimaging
Authors: Zirui Tao,  Won Hwa Kim,  Vikas Singh Seong Jae Hwang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provides a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer's disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.
Multi-Stage Pathological Image Classification Using Semantic Segmentation
Authors: Yusuke Kurose,  Yusuke Mukuta,  Hiroyuki Abe,  Masashi Fukayama,  Akihiko Yoshizawa,  Masanobu Kitagawa,  Tatsuya Harada Shusuke Takahama
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigapixel resolution considering the available memory capacity. Most of the previous works divide high resolution WSIs into small image patches and separately input them into the model to classify it as a tumor or a normal tissue. However, patch-based classification uses only patch-scale local information but ignores the relationship between neighboring patches. If we consider the relationship of neighboring patches and global features, we can improve the classification performance. In this paper, we propose a new model structure combining the patch-based classification model and whole slide-scale segmentation model in order to improve the prediction performance of automatic pathological diagnosis. We extract patch features from the classification model and input them into the segmentation model to obtain a whole slide tumor probability heatmap. The classification model considers patch-scale local features, and the segmentation model can take global information into account. We also propose a new optimization method that retains gradient information and trains the model partially for end-to-end learning with limited GPU memory capacity. We apply our method to the tumor/normal prediction on WSIs and the classification performance is improved compared with the conventional patch-based method.
Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation
Authors: Yang Cong,  Gan Sun,  Dongdong Hou Jiahua Dong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Weakly-supervised learning under image-level labels supervision has been widely applied to semantic segmentation of medical lesions regions. However, 1) most existing models rely on effective constraints to explore the internal representation of lesions, which only produces inaccurate and coarse lesions regions; 2) they ignore the strong probabilistic dependencies between target lesions dataset (e.g., enteroscopy images) and well-to-annotated source diseases dataset (e.g., gastroscope images). To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task. More specifically, a pseudo label generator is proposed to leverage seed information to generate highly-confident pseudo pixel labels by incorporating class balance and super-pixel spatial prior. It can iteratively include more hard-to-transfer samples from weakly-labeled target dataset into training set. Afterwards, dynamically-searched feature centroids for same class among different datasets are aligned by accumulating previously-learned features. Meanwhile, adversarial learning is also employed in this paper, to narrow the gap between the lesions among different datasets in output space. Finally, we build a new medical endoscopic dataset with 3659 images collected from more than 1100 volunteers. Extensive experiments on our collected dataset and several benchmark datasets validate the effectiveness of our model.
Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network
Authors: Lior Wolf,  Lior Golgher,  Pablo Blinder Shir Gur
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: The task of blood vessel segmentation in microscopy images is crucial for many diagnostic and research applications. However, vessels can look vastly different, depending on the transient imaging conditions, and collecting data for supervised training is laborious. We present a novel deep learning method for unsupervised segmentation of blood vessels. The method is inspired by the field of active contours and we introduce a new loss term, which is based on the morphological Active Contours Without Edges (ACWE) optimization method. The role of the morphological operators is played by novel pooling layers that are incorporated to the network's architecture. We demonstrate the challenges that are faced by previous supervised learning solutions, when the imaging conditions shift. Our unsupervised method is able to outperform such previous methods in both the labeled dataset, and when applied to similar but different datasets. Our code, as well as efficient pytorch reimplementations of the baseline methods VesselNN and DeepVess are attached as supplementary.
GLAMpoints: Greedily Learned Accurate Match Points
Authors: Stefanos Apostolopoulos,  Agata Mosinska,  Samuel Stucky,  Carlos Ciller,  Sandro De Zanet Prune Truong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We introduce a novel CNN-based feature point detector - Greedily Learned Accurate Match Points (GLAMpoints) - learned in a semi-supervised manner. Our detector extracts repeatable, stable interest points with a dense coverage, specifically designed to maximize the correct matching in a specific domain, which is in contrast to conventional techniques that optimize indirect metrics. In this paper, we apply our method on challenging retinal slitlamp images, for which classical detectors yield unsatisfactory results due to low image quality and insufficient amount of low-level features. We show that GLAMpoints significantly outperforms classical detectors as well as state-of-the-art CNN-based methods in matching and registration quality for retinal images.
Feature Weighting and Boosting for Few-Shot Segmentation
Authors: Sinisa Todorovic Khoi Nguyen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: This paper is about few-shot segmentation of foreground objects in images. We train a CNN on small subsets of training images, each mimicking the few-shot setting. In each subset, one image serves as the query and the other(s) as support image(s) with ground-truth segmentation. The CNN first extracts feature maps from the query and support images. Then, a class feature vector is computed as an average of the support's feature maps over the known foreground. Finally, the target object is segmented in the query image by using a cosine similarity between the class feature vector and the query's feature map. We make two contributions by: (1) Improving discriminativeness of features so their activations are high on the foreground and low elsewhere; and (2) Boosting inference with an ensemble of experts guided with the gradient of loss incurred when segmenting the support images in testing. Our evaluations on the PASCAL-5i and COCO-20i datasets demonstrate that we significantly outperform existing approaches.
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
Authors: Yipeng Qin,  Peter Wonka Rameen Abdal
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHD dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.
Controllable Artistic Text Style Transfer via Shape-Matching GAN
Authors: Zhangyang Wang,  Zhaowen Wang,  Ning Xu,  Jiaying Liu,  Zongming Guo Shuai Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Artistic text style transfer is the task of migrating the style from a source image to the target text to create artistic typography. Recent style transfer methods have considered texture control to enhance usability. However, controlling the stylistic degree in terms of shape deformation remains an important open challenge. In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter. Our key contribution is a novel bidirectional shape matching framework to establish an effective glyph-style mapping at various deformation levels without paired ground truth. Based on this idea, we propose a scale-controllable module to empower a single network to continuously characterize the multi-scale shape features of the style image and transfer these features to the target text. The proposed method demonstrates its superiority over previous state-of-the-arts in generating diverse, controllable and high-quality stylized text.
Understanding Generalized Whitening and Coloring Transform for Universal Style Transfer
Authors: Tai-Yin Chiu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Style transfer is a task of rendering images in the styles of other images. In the past few years, neural style transfer has achieved a great success in this task, yet suffers from either the inability to generalize to unseen style images or fast style transfer. Recently, an universal style transfer technique that applies zero-phase component analysis (ZCA) for whitening and coloring image features realizes fast and arbitrary style transfer. However, using ZCA for style transfer is empirical and does not have any theoretical support. In addition, other whitening and coloring transforms (WCT) than ZCA have not been investigated. In this report, we generalize ZCA to the general form of WCT, provide an analytical performance analysis from the angle of neural style transfer, and show why ZCA is a good choice for style transfer among different WCTs and why some WCTs are not well applicable for style transfer.
Learning Implicit Generative Models by Matching Perceptual Features
Authors: Youssef Mroueh,  Inkit Padhi,  Pierre Dognin Cicero Nogueira dos Santos
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution. However, the efficacy of PFs as key source of information for learning generative models is not well studied. We investigate here the use of PFs in the context of learning implicit generative models through moment matching (MM). More specifically, we propose a new effective MM approach that learns implicit generative models by performing mean and covariance matching of features extracted from pretrained ConvNets. Our proposed approach improves upon existing MM methods by: (1) breaking away from the problematic min/max game of adversarial learning; (2) avoiding online learning of kernel functions; and (3) being efficient with respect to both number of used moments and required minibatch size. Our experimental results demonstrate that, due to the expressiveness of PFs from pretrained deep ConvNets, our method achieves state-of-the-art results for challenging benchmarks.
Free-Form Image Inpainting With Gated Convolution
Authors: Zhe Lin,  Jimei Yang,  Xiaohui Shen,  Xin Lu,  Thomas S. Huang Jiahui Yu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We present a generative image inpainting system to complete images with free-form mask and guidance. The system is based on gated convolutions learned from millions of images without additional labelling efforts. The proposed gated convolution solves the issue of vanilla convolution that treats all input pixels as valid ones, generalizes partial convolution by providing a learnable dynamic feature selection mechanism for each channel at each spatial location across all layers. Moreover, as free-form masks may appear anywhere in images with any shape, global and local GANs designed for a single rectangular mask are not applicable. Thus, we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches. SN-PatchGAN is simple in formulation, fast and stable in training. Results on automatic image inpainting and user-guided extension demonstrate that our system generates higher-quality and more flexible results than previous methods. Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks and edit faces. Code, demo and models are available at: https://github.com/JiahuiYu/generative_inpainting.
FiNet: Compatible and Diverse Fashion Image Inpainting
Authors: Zuxuan Wu,  Weilin Huang,  Matthew R. Scott,  Larry S. Davis Xintong Han
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Visual compatibility is critical for fashion analysis, yet is missing in existing fashion image synthesis systems. In this paper, we propose to explicitly model visual compatibility through fashion image inpainting. We present Fashion Inpainting Networks (FiNet), a two-stage image-to-image generation framework that is able to perform compatible and diverse inpainting. Disentangling the generation of shape and appearance to ensure photorealistic results, our framework consists of a shape generation network and an appearance generation network. More importantly, for each generation network, we introduce two encoders interacting with one another to learn latent codes in a shared compatibility space. The latent representations are jointly optimized with the corresponding generation network to condition the synthesis process, encouraging a diverse set of generated results that are visually compatible with existing fashion garments. In addition, our framework is readily extended to clothing reconstruction and fashion transfer. Extensive experiments on fashion synthesis quantitatively and qualitatively demonstrate the effectiveness of our method.
InGAN: Capturing and Retargeting the "DNA" of a Natural Image
Authors: Shai Bagon,  Phillip Isola,  Michal Irani Assaf Shocher
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution. However, each natural image has its own internal statistics, captured by its unique distribution of patches. In this paper we propose an "Internal GAN" (InGAN) -- an image-specific GAN -- which trains on a single input image and learns its internal distribution of patches. It is then able to synthesize a plethora of new natural images of significantly different sizes, shapes and aspect-ratios - all with the same internal patch-distribution (same "DNA") as the input image. In particular, despite large changes in global size/shape of the image, all elements inside the image maintain their local size/shape. InGAN is fully unsupervised, requiring no additional data other than the input image itself. Once trained on the input image, it can remap the input to any size or shape in a single feedforward pass, while preserving the same internal patch distribution. InGAN provides a unified framework for a variety of tasks, bridging the gap between textures and natural images.
Seeing What a GAN Cannot Generate
Authors: Jun-Yan Zhu,  Jonas Wulff,  William Peebles,  Hendrik Strobelt,  Bolei Zhou,  Antonio Torralba David Bau
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Despite the success of Generative Adversarial Networks (GANs), mode collapse remains a serious issue during GAN training. To date, little work has focused on understanding and quantifying which modes have been dropped by a model. In this work, we visualize mode collapse at both the distribution level and the instance level. First, we deploy a semantic segmentation network to compare the distribution of segmented objects in the generated images with the target distribution in the training set. Differences in statistics reveal object classes that are omitted by a GAN. Second, given the identified omitted object classes, we visualize the GAN's omissions directly. In particular, we compare specific differences between individual photos and their approximate inversions by a GAN. To this end, we relax the problem of inversion and solve the tractable problem of inverting a GAN layer instead of the entire generator. Finally, we use this framework to analyze several recent GANs trained on multiple datasets and identify their typical failure cases.
COCO-GAN: Generation by Parts via Conditional Coordinating
Authors: Chia-Che Chang,  Yu-Sheng Chen,  Da-Cheng Juan,  Wei Wei,  Hwann-Tzong Chen Chieh Hubert Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose COnditional COordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition. On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity. Despite the full images are never manipulated during training, we show that COCO-GAN can produce state-of-the-art-quality full images during inference. We further demonstrate a variety of novel applications enabled by our coordinate-aware framework. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called "beyond-boundary generation". We then showcase panorama generation within a cylindrical coordinate system that inherently preserves horizontally cyclic topology. On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.
Neural Turtle Graphics for Modeling City Road Layouts
Authors: Daiqing Li,  David Acuna,  Amlan Kar,  Maria Shugrina,  Xinkai Wei,  Ming-Yu Liu,  Antonio Torralba,  Sanja Fidler Hang Chu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the road layout using a graph where nodes in the graph represent control points and edges in the graph represents road segments. NTG is a sequential generative model parameterized by a neural network. It iteratively generates a new node and an edge connecting to an existing node conditioned on the current graph. We train NTG on Open Street Map data and show it outperforms existing approaches using a set of diverse performance metrics. Moreover, our method allows users to control styles of generated road layouts mimicking existing cities as well as to sketch a part of the city road layout to be synthesized. In addition to synthesis, the proposed NTG finds uses in an analytical task of aerial road parsing. Experimental results show that it achieves state-of-the-art performance on the SpaceNet dataset.
Texture Fields: Learning Texture Representations in Function Space
Authors: Lars Mescheder,  Michael Niemeyer,  Thilo Strauss,  Andreas Geiger Michael Oechsle
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2019 (Main Conference Track), 2019

Abstract: In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models.