pwc-archive/papers-with-abstracts huggingface dataset:
Content-Aware Multi-Level Guidance for Interactive Instance Segmentation
Authors: Soumajit Majumder,  Angela Yao
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate content-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform existing approaches that require state-of-the-art segmentation networks pre-trained on large scale segmentation datasets. We demonstrate the effectiveness of our proposed transformation strategy through comprehensive experimentation in which we significantly raise state-of-the-art on four standard interactive segmentation benchmarks.
Learning Active Contour Models for Medical Image Segmentation
Authors: Xu Chen,  Bryan M. Williams,  Srinivasa R. Vallabhaneni,  Gabriela Czanner,  Rachel Williams,  Yalin Zheng
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is an important step in medical image processing and has been widely studied and developed for refinement of clinical analysis and applications. New models based on deep learning have improved results but are restricted to pixel-wise fitting of the segmentation map. Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning. Specifically, we propose a new loss function which incorporates area and size information and integrates this into a dense deep learning model. We evaluated our approach on a dataset of more than 2,000 cardiac MRI scans. Our results show that the proposed loss function outperforms other mainstream loss function Cross-entropy on two common segmentation networks. Our loss function is robust while using different hyperparameter lambda.
Local Features and Visual Words Emerge in Activations
Authors: Oriane Simeoni,  Yannis Avrithis,  Ondrej Chum
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Retrieval, Retrieval

We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations by global pooling, as in recent state-of-the-art work. However, the same sparse 3D activation tensor is also approximated by a collection of local features. These local features are then robustly matched to approximate the optimal alignment of the tensors. This happens without any network modification, additional layers or training. No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process.  We experimentally show that the proposed method achieves the state-of-the-art performance on standard benchmarks across different network architectures and different global pooling methods. The highest gain in performance is achieved when diffusion on the nearest-neighbor graph of global descriptors is initiated from spatially verified images.
Hyperspectral Image Super-Resolution With Optimized RGB Guidance
Authors: Ying Fu,  Tao Zhang,  Yinqiang Zheng,  Debing Zhang,  Hua Huang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Hyperspectral Image Super-Resolution, Image Super-Resolution, Super-Resolution

 To overcome the limitations of existing hyperspectral cameras on   spatial/temporal resolution, fusing a low resolution hyperspectral image (HSI)   with a high resolution RGB (or multispectral) image into a high resolution HSI   has been prevalent. Previous methods for this fusion task usually employ   hand-crafted priors to model the underlying structure of the latent high   resolution HSI, and the effect of the camera spectral response (CSR) of the   RGB camera on super-resolution accuracy has rarely been investigated. In this   paper, we first present a simple and efficient convolutional neural network   (CNN) based method for HSI super-resolution in an unsupervised way, without   any prior training. Later, we append a CSR optimization layer onto the HSI   super-resolution network, either to automatically select the best CSR in a   given CSR dataset, or to design the optimal CSR under some physical   restrictions. Experimental results show our method outperforms the   state-of-the-arts, and the CSR optimization can further boost the accuracy of   HSI super-resolution.
PMS-Net: Robust Haze Removal Based on Patch Map for Single Images
Authors: Wei-Ting Chen,  Jian-Jiun Ding,  Sy-Yen Kuo
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Computational Phenotyping, Denoising, Image Dehazing, Image Restoration, Nonhomogeneous Image Dehazing, Rain Removal, Single Image Dehazing, Single Image Deraining, Single Image Haze Removal

In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation and color distortion. Therefore, in this paper, we designed an adaptive and automatic patch size selection model called the Patch Map Selection Network (PMS-Net) to select the patch size corresponding to each pixel. This network is designed based on the convolutional neural network (CNN), which can generate the patch map from the image to image. Experimental results on both synthesized and real-world hazy images show that, with the combination of the proposed PMS-Net, the performance in haze removal is much better than that of other state-of-the-art algorithms and we can address the problems caused by the fixed patch size.
Task Agnostic Meta-Learning for Few-Shot Learning
Authors: Muhammad Abdullah Jamal,  Guo-Jun Qi
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Few-Shot Learning, General Classification, Meta-Learning, Reinforcement Learning

Meta-learning approaches have been proposed to tackle the few-shot learning problem. Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile  when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-Learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined. Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.
Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning
Authors: Mahdi S. Hosseini,  Lyndon Chan,  Gabriel Tse,  Michael Tang,  Jun Deng,  Sajad Norouzi,  Corwyn Rowsell,  Konstantinos N. Plataniotis,  Savvas Damaskinos
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diagnostic, Multi-Label Learning

In recent years, computer vision techniques have made large advances in image recognition and been applied to aid radiological diagnosis. Computational pathology aims to develop similar tools for aiding pathologists in diagnosing digitized histopathological slides, which would improve diagnostic accuracy and productivity amidst increasing workloads. However, there is a lack of publicly-available databases of (1) localized patch-level images annotated with (2) a large range of Histological Tissue Type (HTT). As a result, computational pathology research is constrained to diagnosing specific diseases or classifying tissues from specific organs, and cannot be readily generalized to handle unexpected diseases and organs.  In this paper, we propose a new digital pathology database, the "Atlas of Digital Pathology" (or ADP), which comprises of 17,668 patch images extracted from 100 slides annotated with up to 57 hierarchical HTTs. Our data is generalized to different tissue types across different organs and aims to provide training data for supervised multi-label learning of patch-level HTT in a digitized whole slide image. We demonstrate the quality of our image labels through pathologist consultation and by training three state-of-the-art neural networks on tissue type classification. Quantitative results support the visually consistency of our data and we demonstrate a tissue type-based visual attention aid as a sample tool that could be developed from our database.
Perturbation Analysis of the 8-Point Algorithm: A Case Study for Wide FoV Cameras
Authors: Thiago L. T. da Silveira,  Claudio R. Jung
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Translation

This paper presents a perturbation analysis for the estimate of epipolar matrices using the 8-Point Algorithm (8-PA). Our approach explores existing bounds for singular subspaces and relates them to the 8-PA, without assuming any kind of error distribution for the matched features. In particular, if we use unit vectors as homogeneous image coordinates, we show that having a wide spatial distribution of matched features in both views tends to generate lower error bounds for the epipolar matrix error. Our experimental validation indicates that the bounds and the effective errors tend to decrease as the camera Field of View (FoV) increases, and that using the 8-PA for spherical images (that present 360degx180deg FoV) leads to accurate essential matrices.  As an additional contribution, we present bounds for the direction of the translation vector extracted from the essential matrix based on singular subspace analysis.
StereoDRNet: Dilated Residual StereoNet
Authors: Rohan Chabra,  Julian Straub,  Christopher Sweeney,  Richard Newcombe,  Henry Fuchs
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction, Stereo Depth Estimation

We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, predicts view-consistent disparity and occlusion maps that helps the fusion system to produce geometrically consistent reconstructions. We utilize 3D dilated convolutions in our proposed cost filtering network that yields better filtering while almost halving the computational cost in comparison to state of the art cost filtering architectures. For feature extraction we use the Vortex Pooling architecture. The proposed method achieves state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo benchmarks. Finally, we demonstrate that our system is able to produce high fidelity 3D scene reconstructions that outperforms the state of the art stereo system.
Deep Single Image Camera Calibration With Radial Distortion
Authors: Manuel Lopez,  Roger Mari,  Pau Gargallo,  Yubin Kuang,  Javier Gonzalez-Jimenez,  Gloria Haro
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Camera Calibration, distortion correction

Single image calibration is the problem of predicting the camera parameters from one image. This problem is of importance when dealing with images collected in uncontrolled conditions by non-calibrated cameras, such as crowd-sourced applications. In this work we propose a method to predict extrinsic (tilt and roll) and intrinsic (focal length and radial distortion) parameters from a single image. We propose a parameterization for radial distortion that is better suited for learning than directly predicting the distortion parameters. Moreover, predicting additional heterogeneous variables exacerbates the problem of loss balancing. We propose a new loss function based on point projections to avoid having to balance heterogeneous loss terms. Our method is, to our knowledge, the first to jointly estimate the tilt, roll, focal length, and radial distortion parameters from a single image. We thoroughly analyze the performance of the proposed method and the impact of the improvements and compare with previous approaches for single image radial distortion correction.
Action4D: Online Action Recognition in the Crowd and Clutter
Authors: Quanzeng You,  Hao Jiang
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Temporal Action Localization

Recognizing every person's action in a crowded and cluttered environment is a challenging task in computer vision. We propose to tackle this challenging problem using a holistic 4D "scan" of a cluttered scene to include every detail about the people and environment. This leads to a new problem, i.e., recognizing multiple people's actions in the cluttered 4D representation. At the first step, we propose a new method to track people in 4D, which can reliably detect and follow each person in real time. Then, we build a new deep neural network, the Action4DNet, to recognize the action of each tracked person. Such a model gives reliable and accurate results in the real-world settings. We also design an adaptive 3D convolution layer and a novel discriminative temporal feature learning objective to further improve the performance of our model. Our method is invariant to camera view angles, resistant to clutter and able to handle crowd. The experimental results show that the proposed method is fast, reliable and accurate. Our method paves the way to action recognition in the real-world applications and is ready to be deployed to enable smart homes, smart factories and smart stores.
Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recognition
Authors: Jiayu Dong,  Huicheng Zheng,  Lina Lian
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Recognition, Robust Face Recognition

Sparse representation based methods have successfully put forward a general framework for robust face recognition through linear reconstruction and sparsity constraints. However, residual modeling in existing works is not yet robust enough when dealing with dense noise. In this paper, we aim at recognizing identities from faces with varying levels of noises of various forms such as occlusion, pixel corruption, or disguise, and take improving the fitting ability of the error model as the key to addressing this problem. To fully capture the characteristics of different noises, we propose a mixed model combining robust sparsity constraint and low-rank constraint, which can deal with random errors and structured errors simultaneously. For random noises such as pixel corruption, we adopt a Laplacian-uniform mixed function for fitting the error distribution. For structured errors like continuous occlusion or disguise, we utilize robust nuclear norm to constrain the rank of the error matrix. An effective iterative reweighted algorithm is then developed to solve the proposed model. Comprehensive experiments were conducted on several benchmark databases for robust face recognition, and the overall results demonstrate that our model is most robust against various kinds of noises, when compared with state-of-the-art methods.
Generalizing Eye Tracking With Bayesian Adversarial Learning
Authors: Kang Wang,  Rui Zhao,  Hui Su,  Qiang Ji
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Bayesian Inference, Gaze Estimation

Existing appearance-based gaze estimation approaches with CNN have poor generalization performance. By systematically studying this issue, we identify three major factors: 1) appearance variations; 2) head pose variations and 3) over-fitting issue with point estimation. To improve the generalization performance, we propose to incorporate adversarial learning and Bayesian inference into a unified framework. In particular, we first add an adversarial component into traditional CNN-based gaze estimator so that we can learn features that are gaze-responsive but can generalize to appearance and pose variations. Next, we extend the point-estimation based deterministic model to a Bayesian framework so that gaze estimation can be performed using all parameters instead of only one set of parameters. Besides improved performance on several benchmark datasets, the proposed method also enables online adaptation of the model to new subjects/environments, demonstrating the potential usage for practical real-time eye tracking applications.
Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection
Authors: Xuesong Niu,  Hu Han,  Songfan Yang,  Yan Huang,  Shiguang Shan
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Unit Detection, Diversity, Facial Action Unit Detection

Encoding individual facial expressions via action units (AUs) coded by the Facial Action Coding System (FACS) has been found to be an effective approach in resolving the ambiguity issue among different expressions. While a number of methods have been proposed for AU detection, robust AU detection in the wild remains a challenging problem because of the diverse baseline AU intensities across individual subjects, and the weakness of appearance signal of AUs. To resolve these issues, in this work, we propose a novel AU detection method by utilizing local information and the relationship of individual local face regions. Through such a local relationship learning, we expect to utilize rich local information to improve the AU detection robustness against the potential perceptual inconsistency of individual local regions. In addition, considering the diversity in the baseline AU intensities of individual subjects, we further regularize local relationship learning via person-specific face shape information, i.e., reducing the influence of person-specific shape information, and obtaining more AU discriminative features. The proposed approach outperforms the state-of-the-art methods on two widely used AU detection datasets in the public domain (BP4D and DISFA).
AdaptiveFace: Adaptive Margin and Sampling for Face Recognition
Authors: Hao Liu,  Xiangyu Zhu,  Zhen Lei,  Stan Z. Li
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Recognition

Training large-scale unbalanced data is the central topic in face recognition. In the past two years, face recognition has achieved remarkable improvements due to the introduction of margin based Softmax loss. However, these methods have an implicit assumption that all the classes possess sufficient samples to describe its distribution, so that a manually set margin is enough to equally squeeze each intra-class variations. However, real face datasets are highly unbalanced, which means the classes have tremendously different numbers of samples. In this paper, we argue that the margin should be adapted to different classes. We propose the Adaptive Margin Softmax to adjust the margins for different classes adaptively. In addition to the unbalance challenge, face data always consists of large-scale classes and samples. Smartly selecting valuable classes and samples to participate in the training makes the training more effective and efficient. To this end, we also make the sampling process adaptive in two folds: Firstly, we propose the Hard Prototype Mining to adaptively select a small number of hard classes to participate in classification. Secondly, for data sampling, we introduce the Adaptive Data Sampling to find valuable samples for training adaptively. We combine these three parts together as AdaptiveFace. Extensive analysis and experiments on LFW, LFW BLUFR and MegaFace show that our method performs better than state-of-the-art methods using the same network architecture and training dataset. Code is available at https://github.com/haoliu1994/AdaptiveFace.
Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering
Authors: Xinglin Piao,  Yongli Hu,  Junbin Gao,  Yanfeng Sun,  Baocai Yin
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering

Unsupervised clustering for high-dimension data (such as imageset or video) is a hard issue in data processing and data mining area since these data always lie on a manifold (such as Grassmann manifold). Inspired of Low Rank representation theory, researchers proposed a series of effective clustering methods for high-dimension data with non-linear metric. However, most of these methods adopt the traditional single nuclear norm as the relaxation of the rank function, which would lead to suboptimal solution deviated from the original one. In this paper, we propose a new low rank model for high-dimension data clustering task on Grassmann manifold based on the Double Nuclear norm which is used to better approximate the rank minimization of matrix. Further, to consider the inner geometry or structure of data space, we integrated the adaptive Laplacian regularization to construct the local relationship of data samples. The proposed models have been assessed on several public datasets for imageset clustering. The experimental results show that the proposed models outperform the state-of-the-art clustering ones.
Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation
Authors: Artur Grigorev,  Artem Sevastopolsky,  Alexander Vakhitov,  Victor Lempitsky
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Resynthesis

We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body. Rather than working directly with colors of texture elements, the inpainting network estimates an appropriate source location in the input image for each element of the body surface. This correspondence field between the input image and the texture is then further warped into the target image coordinate frame based on the desired pose, effectively establishing the correspondence between the source and the target view even when the pose change is drastic. The final convolutional network then uses the established correspondence and all other available information to synthesize the output image.   A  fully-convolutional  architecture with deformable skip connections guided by the estimated correspondence field is used.  We show state-of-the-art result for pose-guided image synthesis. Additionally, we demonstrate the performance of our system for garment transfer and pose-guided face resynthesis.