pwc-archive/papers-with-abstracts huggingface dataset:
Consensus Convolutional Sparse Coding
Authors: Biswarup Choudhury, Robin Swanson, Felix Heide, Gordon Wetzstein, Wolfgang Heidrich
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Demosaicking, Image Reconstruction

Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning
Authors: Calvin Murdock, Fernando De La Torre
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Denoising, Dimensionality Reduction, Transfer Learning

Subspace learning is one of the most foundational tasks in computer vision with applications ranging from dimensionality reduction to data denoising. As geometric objects, subspaces have also been successfully used for efficiently representing certain types of invariant data. However, methods for subspace learning from subspace-valued data have been notably absent due to incompatibilities with standard problem formulations. To fill this void, we introduce Approximate Grassmannian Intersections (AGI), a novel geometric interpretation of subspace learning posed as finding the approximate intersection of constraint sets on a Grassmann manifold. Our approach can naturally be applied to input subspaces of varying dimension while reducing to standard subspace learning in the case of vector-valued data. Despite the nonconvexity of our problem, its globally-optimal solution can be found using a singular value decomposition. Furthermore, we also propose an efficient, general optimization approach that can incorporate additional constraints to encourage properties such as robustness. Alongside standard subspace applications, AGI also enables the novel task of transfer learning via subspace completion. We evaluate our approach on a variety of applications, demonstrating improved invariance and generalization over vector-valued alternatives.
Summarization and Classification of Wearable Camera Streams by Learning the Distributions Over Deep Features of Out-Of-Sample Image Sequences
Authors: Alessandro Perina, Sadegh Mohammadi, Nebojsa Jojic, Vittorio Murino
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: General Classification

A popular approach to training classifiers of new image classes is to use lower levels of a pre-trained feed-forward neural network and retrain only the top. Thus, most layers simply serve as highly nonlinear feature extractors. While these features were found useful for classifying a variety of scenes and objects, previous work also demonstrated unusual levels of sensitivity to the input especially for images which are veering too far away from the training distribution. This can lead to surprising results as an imperceptible change in an image can be enough to completely change the predicted class. This occurs in particular in applications involving personaldata, typically acquired with wearable cameras (e.g., visual lifelogs), where the problem is also made more complex by the dearth of new labeled training data that make supervised learning with deep models difficult. To alleviate these problems, in this paper we propose a new generative model that captures the feature distribution in new data. Its latent space then becomes more representative of the new data, while still retaining the generalization properties. In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks
Authors: Feihu Zhang, Benjamin W. Wah
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Denoising, Diversity, image-classification, Image Classification, Image Super-Resolution, Meta-Learning, Super-Resolution

Data diversity in terms of types, styles, as well as radiometric, exposure and texture conditions widely exists in training and test data of vision applications. However, learning in traditional neural networks (NNs) only tries to find a model with fixed parameters that optimize the average behavior over all inputs, without using data-specific properties. In this paper, we develop a meta-level NN (MLNN) model that learns meta-knowledge on data-specific properties of images during learning and that dynamically adapts its weights during application according to the properties of the images input. MLNN consists of two parts: the dynamic supplementary NN (SNN) that learns meta-information on each type of inputs, and the fixed base-level NN (BLNN) that incorporates the meta-information from SNN into its weights at run time to realize the generalization for each type of inputs. We verify our approach using over ten network architectures under various application scenarios and loss functions. In low-level vision applications on image super-resolution and denoising, MLNN has 0.1 0.3 dB improvements on PSNR, whereas for high-level image classification, MLNN has accuracy improvement of 0.4 0.6% for Cifar10 and 1.2 2.1% for ImageNet when compared to convolutional NNs (CNNs). Improvements are more pronounced as the scale or diversity of data is increased.
Active Learning for Human Pose Estimation
Authors: Buyu Liu, Vittorio Ferrari
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Active Learning, Pose Estimation

Annotating human poses in realistic scenes is very time consuming, yet necessary for training human pose estimators. We propose to address this problem in an active learning framework, which alternates between requesting the most useful annotations among a large set of unlabelled images, and re-training the pose estimator. To this end, (1) we propose an uncertainty estimator specific for body joint predictions, which takes into account the spatial distribution of the responses of the current pose estimator on the unlabelled images; (2) we propose a dynamic combination of influence and uncertainty cues, where their weights vary during the active learning process according to the reliability of the current pose estimator; (3) we introduce a computer assisted annotation interface, which reduces the time necessary for a human annotator to click on a joint by discretizing the image into regions generated by the current pose estimator. Experiments using the MPII and LSP datasets with both simulated and real annotators show that (1) the proposed active selection scheme outperforms several baselines; (2) our computer-assisted interface can further reduce annotation effort; and (3) our technique can further improve the performance of a pose estimator even when starting from an already strong one.performance in 23% annotation time.
Interleaved Group Convolutions
Authors: Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary group convolution is a spatial convolution, while on each partition in secondary group convolution, the convolution is a point-wise convolution; (ii) the channels in the same secondary partition come from different primary partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion, and the Xception block are special cases of interleaved group convolutions. Empirical results over standard benchmarks, CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.
Learning-Based Cloth Material Recovery From Video
Authors: Shan Yang, Junbang Liang, Ming C. Lin
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

Image understanding enables better reconstruction of the physical world from images and videos. Existing methods focus largely on geometry and visual appearance of the reconstructed scene. In this paper, we extend the frontier in image understanding and present a new technique to recover the material properties of cloth from a video.Previous cloth material recovery methods often require markers or complex experimental set-up to acquire physical properties, or are limited to certain types of images/videos. Our approach takes advantages of the appearance changes of the moving cloth to infer its physical properties. To extract information about the cloth, our method characterizes both the motion space and the visual appearance of the cloth geometry. We apply the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM) neural network to material recovery of cloth properties from videos. We also exploit simulated data to help statistical learning of mapping between the visual appearance and motion dynamics of the cloth. The effectiveness of our method is demonstrated via validation using simulated datasets and real-life recorded videos.
Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings
Authors: Sara Shaheen, Lama Affara, Bernard Ghanem
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Compression

Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in turn can be cast as a non-standard CSC model that considers the line drawing formation process from parametric curves. These curves are learned to optimize the fit between the model and a specific set of line drawings. Parametric representation of sketches is vital in enabling automatic sketch analysis, synthesis and manipulation. A couple of sketch manipulation examples are demonstrated in this work. Consequently, our novel method is expected to provide a reliable and automatic method for parametric sketch description. Through experiments, we empirically validate the convergence of our method to a feasible solution.
Makeup-Go: Blind Reversion of Portrait Edit
Authors: Ying-Cong Chen, Xiaoyong Shen, Jiaya Jia
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: regression

Virtual face beautification (or markup) becomes common operations in camera or image processing Apps, which is actually deceiving. In this paper, we propose the task of restoring a portrait image from this process. As the first attempt along this line, we assume unknown global operations on human faces and aim to tackle the two issues of skin smoothing and skin color change. These two tasks, intriguingly, impose very different difficulties to estimate subtle details and major color variation. We propose a Component Regression Network (CRN) and address the limitation of using Euclidean loss in blind reversion. CRN maps the edited portrait images back to the original ones without knowing beautification operation details. Our experiments demonstrate effectiveness of the system for this novel task.
Shadow Detection With Conditional Generative Adversarial Networks
Authors: Vu Nguyen, Tomas F. Yago Vicente, Maozheng Zhao, Minh Hoai, Dimitris Samaras
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Sensitivity, Shadow Detection

We introduce scGAN, a novel extension of conditional Generative Adversarial Networks (GAN) tailored for the challenging problem of shadow detection in images. Previous methods for shadow detection focus on learning the local appearance of shadow regions, while using limited local context reasoning in the form of pairwise potentials in a Conditional Random Field. In contrast, the proposed adversarial approach is able to model higher level relationships and global scene characteristics. We train a shadow detector that corresponds to the generator of a conditional GAN, and augment its shadow accuracy by combining the typical GAN loss with a data loss term. Due to the unbalanced distribution of the shadow labels, we use weighted cross entropy. With the standard GAN architecture, properly setting the weight for the cross entropy would require training multiple GANs, a computationally expensive grid procedure. In scGAN, we introduce an additional sensitivity parameter w to the generator. The proposed approach effectively parameterizes the loss of the trained detector. The resulting shadow detector is a single network that can generate shadow maps corresponding to different sensitivity levels, obviating the need for multiple models and a costly training procedure. We evaluate our method on the large-scale SBU and UCF shadow datasets, and observe up to 17% error reduction with respect to the previous state-of-the-art method.
Practical and Efficient Multi-View Matching
Authors: Eleonora Maset, Federica Arrigoni, Andrea Fusiello
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

In this paper we propose a novel solution to the multi-view matching problem that, given a set of noisy pairwise correspondences, jointly updates them so as to maximize their consistency. Our method is based on a spectral decomposition, resulting in a closed-form efficient algorithm, in contrast to other iterative techniques that can be found in the literature. Experiments on both synthetic and real datasets show that our method achieves comparable or superior accuracy to state-of-the-art algorithms in significantly less time. We also demonstrate that our solution can efficiently handle datasets of hundreds of images, which is unprecedented in the literature.
Unrolled Memory Inner-Products: An Abstract GPU Operator for Efficient Vision-Related Computations
Authors: Yu-Sheng Lin, Wei-Chao Chen, Shao-Yi Chien
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: GPU, Rolling Shutter Correction

Recently, convolutional neural networks (CNNs) have achieved great success in fields such as computer vision, natural language processing, and artificial intelligence. Many of these applications utilize parallel processing in GPUs to achieve higher performance. However, it remains a daunting task to optimize for GPUs, and most researchers have to rely on vendor-provided libraries for such purposes. In this paper, we discuss an operator that can be used to succinctly express computational kernels in CNNs and various scientific and vision applications. This operator, called Unrolled-Memory-Inner-Product (UMI), is a computationally-efficient operator with smaller code token requirement. Since a naive UMI implementation would increase memory requirement through input data unrolling, we propose a method to achieve optimal memory fetch performance in modern GPUs. We demonstrate this operator by converting several popular applications into the UMI representation and achieve 1.3x-26.4x speedup against frameworks such as OpenCV and Caffe.
Learning to Push the Limits of Efficient FFT-Based Image Deconvolution
Authors: Jakob Kruse, Carsten Rother, Uwe Schmidt
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Deconvolution

This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize existing discriminative approaches by using more powerful regularization, based on convolutional neural networks. Additionally, we propose a simple, yet effective, boundary adjustment method that alleviates the problematic circular convolution assumption, which is necessary for FFT-based deconvolution. We evaluate our approach on two common non-blind deconvolution benchmarks and achieve state-of-the-art results even when including methods which are computationally considerably more expensive.
Visual Odometry for Pixel Processor Arrays
Authors: Laurie Bose, Jianing Chen, Stephen J. Carey, Piotr Dudek, Walterio Mayol-Cuevas
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Edge Detection, Translation, Visual Odometry

We present an approach of estimating constrained motion of a novel Cellular Processor Array (CPA) camera, on which each pixel is capable of limited processing and data storage allowing for fast low power parallel computation to be carried out directly on the focal-plane of the device. Rather than the standard pipeline involved with traditional cameras whereby whole camera images are transferred to a general computer system for processing, our approach performs all computation upon the CPA itself, with the only information being transfered to a standard computer being the camera's estimated motion.This limited data transfer allows for high frame-rate processing at hundreds of hz while consuming less than 1.5 Watts of power.The current implementation is restricted to the estimation of the camera's rotation in yaw and pitch, along with a scaleless estimate of the camera's forward and backward translation. We describe methods of image alignment by gradient descent, edge detection, and image scaling, all of which are performed solely on the CPA device itself and which form the core components of detecting camera motion.
2D-Driven 3D Object Detection in RGB-D Images
Authors: Jean Lahoud, Bernard Ghanem
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: 2D Object Detection, 3D Object Detection, Object, object-detection, Object Detection, Object Detection In Indoor Scenes

In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.
Ray Space Features for Plenoptic Structure-From-Motion
Authors: Yingliang Zhang, Peihong Yu, Wei Yang, Yuanxi Ma, Jingyi Yu
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10

Traditional Structure-from-Motion (SfM) uses images captured by cameras as inputs. In this paper, we explore using light fields captured by plenoptic cameras or camera arrays as inputs. We call this solution plenoptic SfM or P-SfM solution. We first present a comprehensive theory on ray geometry transforms under light field pose variations. We derive the transforms of three typical ray manifolds: rays passing through a point or point-ray manifold, rays passing through a 3D line or ray-line manifold, and rays lying on a common 3D plane or ray-plane manifold. We show that by matching these manifolds across LFs, we can recover light field poses and conduct bundle adjustment in ray space. We validate our theory and framework on synthetic and real data on light fields of different scales: small scale LFs acquired using a LF camera and large scale LFs by a camera array. We show that our P-SfM technique can significantly improve the accuracy and reliability over regular SfM and PnP especially on traditionally challenging scenes where reliable feature point correspondences are difficult to obtain but line or plane correspondences are readily accessible.
Depth Estimation Using Structured Light Flow -- Analysis of Projected Pattern Flow on an Object's Surface
Authors: Ryo Furukawa, Ryusuke Sagawa, Hiroshi Kawasaki
Date: 2017-10-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Depth Estimation

Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive researches have been conducted for solving defocus blur, few researches for motion blur and only solution is to capture with extremely fast shutter speed. In this paper, unlike the previous approaches, we actively utilize motion blur, which we refer to as a light flow, to estimate depth. Analysis reveals that minimum two light flows, which are retrieved from two projected patterns on the object, are required for depth estimation. To retrieve two light flows at the same time, two sets of parallel line patterns are illuminated from two video projectors and the size of motion blur of each line is precisely measured. By analyzing the light flows, i.e. lengths of the blurs, scene depth information is estimated. In the experiments, 3D shapes of fast moving objects, which are inevitably captured with motion blur, are successfully reconstructed by our technique.