pwc-archive/papers-with-abstracts huggingface dataset:
Can Walking and Measuring Along Chord Bunches Better Describe Leaf Shapes?
Authors: Bin Wang, Yongsheng Gao, Changming Sun, Michael Blumenstein, John La Salle
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Translation

Effectively describing and recognizing leaf shapes under arbitrary deformations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing shape by walking along a bunch of chords that pass through the shape to measure the regions trespassed. A novel chord bunch walks (CBW) descriptor is developed through the chord walking that effectively integrates the shape image function over the walked chord to reflect the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chord walks to build a hierarchical framework for a coarse-to-fine description. The proposed CBW descriptor is invariant to rotation, scaling, translation, and mirror transforms. Instead of using the expensive optimal correspondence based matching, an improved Hausdorff distance encoded correspondence information is proposed for efficient yet effective shape matching. In experimental studies, the proposed method obtained substantially higher accuracies with low computational cost over the benchmarks, which indicates the research potential along this direction.
Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory
Authors: Iasonas Kokkinos
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Boundary Detection, GPU, Human Part Segmentation, object-detection, Object Detection, Region Proposal, Saliency Prediction, Segmentation, Semantic Segmentation

In this work we  train in an end-to-end manner a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a  unified architecture.  Such a network can act like a `swiss knife' for vision tasks;  we call it an "UberNet" to indicate its overarching nature.    The main contribution of this work consists in handling challenges that emerge when scaling up to many tasks. We introduce  techniques that facilitate (i) training a deep architecture while relying on diverse training sets and (ii) training many (potentially unlimited) tasks with a limited memory budget.  This allows us to train in an end-to-end manner a unified CNN architecture that jointly handles (a) boundary detection (b) normal estimation (c) saliency estimation (d) semantic segmentation (e) human part segmentation (f) semantic boundary detection, (g) region proposal generation and object detection. We obtain competitive performance while jointly addressing all tasks in 0.7 seconds on a GPU.  Our system will be made publicly available.
Parametric T-Spline Face Morphable Model for Detailed Fitting in Shape Subspace
Authors: Weilong Peng, Zhiyong Feng, Chao Xu, Yong Su
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: subspace methods

Pre-learnt subspace methods, e.g., 3DMMs, are significant exploration for the synthesis of 3D faces by assuming that faces are in a linear class. However, the human face is in a nonlinear manifold, and a new test are always not in the pre-learnt subspace accurately because of the disparity brought by ethnicity, age, gender, etc. In the paper, we propose a parametric T-spline morphable model (T-splineMM) for 3D face representation, which has great advantages of fitting data from an unknown source accurately. In the model, we describe a face by C^2 T-spline surface, and divide the face surface into several shape units (SUs), according to facial action coding system (FACS), on T-mesh instead of on the surface directly. A fitting algorithm is proposed to optimize coefficients of T-spline control point components along pre-learnt identity and expression subspaces, as well as to optimize the details in refinement progress. As any pre-learnt subspace is not complete to handle the variety and details of faces and expressions, it covers a limited span of morphing. SUs division and detail refinement make the model fitting the facial muscle deformation in a larger span of morphing subspace. We conduct experiments on face scan data, kinect data as well as the space-time data to test the performance of detail fitting, robustness to missing data and noise, and to demonstrate the effectiveness of our model. Convincing results are illustrated to demonstrate the effectiveness of our model compared with the popular methods.
Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning
Authors: Rudrasis Chakraborty, Soren Hauberg, Baba C. Vemuri
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

Principal Component Analysis (PCA) is a fundamental method for estimating a linear subspace approximation to high-dimensional data. Many algorithms exist in literature to achieve a statistically robust version of PCA called RPCA.  In this paper, we present a geometric framework for computing the principal linear subspaces in both situations that amounts to computing the intrinsic average on the space of all subspaces (the Grassmann manifold).  Points on this manifold are defined as the subspaces spanned by K-tuples of observations. We show that the intrinsic Grassmann average of these subspaces coincide with the principal components of the observations when they are drawn from a Gaussian distribution. Similar results are also shown to hold for the RPCA. Further, we propose an efficient online algorithm to do subspace averaging which is of linear complexity in terms of number of samples and has a linear convergence rate.  When the data has outliers, our proposed online robust subspace averaging algorithm shows significant performance (accuracy and computation time) gain over a recently published RPCA methods with publicly accessible code.  We have demonstrated competitive performance of our proposed online subspace algorithm method on one synthetic and two real data sets. Experimental results depicting stability of our proposed method are also presented. Furthermore, on two real outlier corrupted datasets, we present comparison experiments showing lower reconstruction error using our online RPCA algorithm.  In terms of reconstruction error and time required, both our algorithms outperform the competition.
Object Co-Skeletonization With Co-Segmentation
Authors: Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu, Junsong Yuan
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Segmentation

Recent advances in the joint processing of images have certainly shown its advantages over the individual processing. Different from the existing works geared towards co-segmentation or co-localization, in this paper, we explore a new joint processing topic: co-skeletonization, which is defined as joint skeleton extraction of common objects in a set of semantically similar images. Object skeletonization in real world images is a challenging problem, because there is no prior knowledge of the object's shape if we consider only a single image. This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help, just as it does for other joint processing problems such as co-segmentation. Noting that skeleton can provide good scribbles for segmentation, and skeletonization, in turn, needs good segmentation, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically. Since it is a new problem, we also construct a benchmark dataset for the co-skeletonization task. Extensive experiments demonstrate that proposed method achieves very competitive results.
Product Split Trees
Authors: Artem Babenko, Victor Lempitsky
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Clustering, Quantization

In this work, we introduce a new kind of spatial partition trees for efficient nearest-neighbor search. Our approach first identifies a set of useful data splitting directions, and then learns a codebook that can be used to encode such directions. We use the product-quantization idea in order to make the effective codebook large, the evaluation of scalar products between the query and the encoded splitting direction very fast, and the encoding itself compact. As a result, the proposed data srtucture (Product Split tree) achieves compact clustering of data points, while keeping the  traversal very efficient. In the nearest-neighbor search experiments on high-dimensional data, product split trees achieved state-of-the-art performance, demonstrating better speed-accuracy tradeoff than other spatial partition trees.
Dynamic FAUST: Registering Human Bodies in Motion
Authors: Federica Bogo, Javier Romero, Gerard Pons-Moll, Michael J. Black
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D geometry

While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data; that is 3D scans of moving non-rigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http://dfaust.is.tue.mpg.de.
DUST: Dual Union of Spatio-Temporal Subspaces for Monocular Multiple Object 3D Reconstruction
Authors: Antonio Agudo, Francesc Moreno-Noguer
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Reconstruction, Clustering, Segmentation

We present an approach to reconstruct the 3D shape of multiple deforming objects from incomplete 2D trajectories acquired by a single camera. Additionally, we simultaneously provide spatial segmentation (i.e., we identify each of the objects in every frame) and temporal clustering (i.e., we split the sequence into primitive actions). This advances existing work, which only tackled the  problem for  one single object and non-occluded tracks. In order to handle several objects at a time from partial observations, we model point  trajectories as a union of spatial and temporal subspaces, and optimize the parameters of both modalities,  the non-observed point tracks and the 3D shape via augmented Lagrange multipliers. The algorithm is fully unsupervised and results in a formulation which does not need initialization. We thoroughly validate the method on challenging scenarios with several human subjects performing different activities which involve complex motions and close interaction. We show our approach achieves state-of-the-art 3D reconstruction results, while it also provides spatial and temporal segmentation.
Light Field Reconstruction Using Deep Convolutional Network on EPI
Authors: Gaochang Wu, Mandan Zhao, Liangyong Wang, Qionghai Dai, Tianyou Chai, Yebin Liu
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

In this paper, we take advantage of the clear texture structure of the epipolar plane image (EPI) in the light field data and model the problem of light field reconstruction from a sparse set of views as a CNN-based angular detail restoration on EPI. We indicate that one of the main challenges in sparsely sampled light field reconstruction is the information asymmetry between the spatial and angular domain, where the detail portion in the angular domain is damaged by undersampling. To balance the spatial and angular information, the spatial high frequency components of an EPI is removed using EPI blur, before feeding to the network. Finally, a non-blind deblur operation is used to recover the spatial detail suppressed by the EPI blur. We evaluate our approach on several datasets including synthetic scenes, real-world scenes and challenging microscope light field data. We demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms. We also show a further application for depth enhancement by using the reconstructed light field.
Noise Robust Depth From Focus Using a Ring Difference Filter
Authors: Jaeheung Surh, Hae-Gon Jeon, Yunwon Park, Sunghoon Im, Hyowon Ha, In So Kweon
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

Depth from focus (DfF) is a method of estimating depth of a scene by using the information acquired through the change of the focus of a camera. Within the framework of DfF, the focus measure (FM) forms the foundation on which the accuracy of the output is determined. With the result from the FM, the role of a DfF pipeline is to determine and recalculate unreliable measurements while enhancing those that are reliable. In this paper, we propose a new FM that more accurately and robustly measures focus, which we call the "ring difference filter" (RDF). FMs can usually be categorized as confident local methods or noise robust non-local methods. RDF's unique ring-and-disk structure allows it to have the advantageous sides of both local and non-local FMs. We then describe an efficient pipeline that utilizes the properties that the RDF brings. Our method is able to reproduce results that are on par with or even better than those of the state-of-the-art, while spending less time in computation.
Improving RANSAC-Based Segmentation Through CNN Encapsulation
Authors: Dustin Morley, Hassan Foroosh
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Segmentation, Iris Recognition, Segmentation, Semantic Segmentation

In this work, we present a method for improving a random sample consensus (RANSAC) based image segmentation algorithm by encapsulating it within a convolutional neural network (CNN). The improvements are gained by gradient descent training on the set of pre-RANSAC filtering and thresholding operations using a novel RANSAC-based loss function, which is geared toward optimizing the strength of the correct model relative to the most convincing false model. Thus, it can be said that our loss function trains the network on metrics that directly dictate the success or failure of the final segmentation rather than metrics that are merely correlated to the success or failure. We demonstrate successful application of this method to a RANSAC method for identifying the pupil boundary in images from the CASIA-IrisV3 iris recognition data set, and we expect that this method could be successfully applied to any RANSAC-based segmentation algorithm.
Bayesian Supervised Hashing
Authors: Zihao Hu, Junxuan Chen, Hongtao Lu, Tongzhen Zhang
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

Among learning based hashing methods, supervised hashing seeks compact binary representation of the training data to preserve semantic similarities. Recent years have witnessed various problem formulations and optimization methods for supervised hashing. Most of them optimize a form of loss function with a regulization term, which can be viewed as a maximum a posterior (MAP) estimation of the hashing codes. However, these approaches are prone to overfitting unless hyperparameters are tuned carefully. To address this problem, we present a novel fully Bayesian treatment for supervised hashing problem, named Bayesian Supervised Hashing (BSH), in which hyperparameters are automatically tuned during optimization. Additionally, by utilizing automatic relevance determination (ARD), we can figure out relative discriminating ability of different hashing bits and select most informative bits among them. Experimental results on three real-world image datasets with semantic information show that BSH can achieve superior performance over state-of-the-art methods with comparable training time.
Mimicking Very Efficient Network for Object Detection
Authors: Quanquan Li, Shengying Jin, Junjie Yan
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, object-detection, Object Detection

Current CNN based object detectors need initialization from pre-trained ImageNet classification models, which are usually time-consuming. In this paper, we present a fully convolutional feature mimic framework to train very efficient CNN based detectors, which do not need ImageNet pre-training and achieve competitive performance as the large and slow models. We add supervision from high-level features of the large networks in training to help the small network better learn object representation. More specifically, we conduct a mimic method for the features sampled from the entire feature map and use a transform layer to map features from the small network onto the same dimension of the large network. In training the small network, we optimize the similarity between features sampled from the same region on the feature maps of both networks. Extensive experiments are conducted on pedestrian and common object detection tasks using VGG, Inception and ResNet. On both Caltech and Pascal VOC, we show that the modified 2.5x accelerated Inception network achieves competitive performance as the full Inception Network. Our faster model runs at 80 FPS for a 1000x1500 large input with only a minor degradation of performance on Caltech.
4D Light Field Superpixel and Segmentation
Authors: Hao Zhu, Qi Zhang, Qing Wang
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Segmentation

Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate the ambiguity. The LFSP is first defined mathematically and then a refocus-invariant metric named LFSP self-similarity is proposed to evaluate the segmentation performance. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the state-of-the-arts in terms of traditional evaluation metrics. Additionally the LFSP self-similarity evaluation under different light field refocus levels shows the refocus-invariance of the proposed algorithm.
Multi-Task Clustering of Human Actions by Sharing Information
Authors: Xiaoqiang Yan, Shizhe Hu, Yangdong Ye
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Clustering, Temporal Action Localization

Sharing information between multiple tasks can enhance the accuracy of human action recognition systems. However, using shared information to improve multi-task human action clustering has never been considered before, and cannot be achieved using existing clustering methods. In this work, we present a novel and effective Multi-Task Information Bottleneck (MTIB) clustering method, which is capable of exploring the shared information between multiple action clustering tasks to improve the performance of individual task. Our motivation is that, different action collections always share many similar action patterns, and exploiting the shared information can lead to improved performance. Specifically, MTIB generally formulates this problem as an information loss minimization function. In this function, the shared information can be quantified by the distributional correlation of clusters in different tasks, which is based on a high-level common vocabulary constructed through a novel agglomerative information maximization method. Extensive experiments on two kinds of challenging data sets, including realistic action data sets (HMDB & UCF50, Olympic & YouTube), and cross-view data sets (IXMAS, WVU), show that the proposed approach compares favorably to the state-of-the-art methods.
Computational Imaging on the Electric Grid
Authors: Mark Sheinin, Yoav Y. Schechner, Kiriakos N. Kutulakos
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7

Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide. To do all this, we built a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid's AC lighting.
LSTM Self-Supervision for Detailed Behavior Analysis
Authors: Biagio Brattoli, Uta Buchler, Anna-Sophia Wahl, Martin E. Schwab, Bjorn Ommer
Date: 2017-07-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diagnostic, Self-Supervised Learning

Behavior analysis provides a crucial non-invasive and easily accessible diagnostic tool for biomedical research. A detailed analysis of posture changes during skilled motor tasks can reveal distinct functional deficits and their restoration during recovery. Our specific scenario is based on a neuroscientific study of rodents recovering from a large sensorimotor cortex stroke and skilled forelimb grasping is being recorded. Given large amounts of unlabeled videos that are recorded during such long-term studies, we seek an approach that captures fine-grained details of posture and its change during rehabilitation without costly manual supervision. Therefore, we utilize self-supervision to automatically learn accurate posture and behavior representations for analyzing motor function. Learning our model depends on the following fundamental elements: (i) limb detection based on a fully convolutional network is ini- tialized solely using motion information, (ii) a novel self- supervised training of LSTMs using only temporal permu- tation yields a detailed representation of behavior, and (iii) back-propagation of this sequence representation also im- proves the description of individual postures. We establish a novel test dataset with expert annotations for evaluation of fine-grained behavior analysis. Moreover, we demonstrate the generality of our approach by successfully applying it to self-supervised learning of human posture on two standard benchmark datasets.