mtybilly/Instruct2DS huggingface dataset:
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features
Authors: Ayan Sinha, Chiho Choi, Karthik Ramani
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose DeepHand to estimate the 3D pose of a hand using depth data from commercial 3D sensors. We discriminatively train convolutional neural networks to output a low dimensional activation feature given a depth map. This activation feature vector is representative of the global or local joint angle parameters of a hand pose. We efficiently identify 'spatial' nearest neighbors to the activation feature, from a database of features corresponding to synthetic depth maps, and store some 'temporal' neighbors from previous frames. Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to to estimate the unknown pose parameters of the input feature vector. Our database of activation features supplements large viewpoint coverage and our hierarchical estimation of pose parameters is robust to occlusions. We show that our approach  compares favorably to state-of-the-art methods while achieving real time performance (32 FPS) on a standard computer.
Multi-Oriented Text Detection With Fully Convolutional Networks
Authors: Zheng Zhang, Chengquan Zhang, Wei Shen, Cong Yao, Wenyu Liu, Xiang Bai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we propose an unconventional approach for text detection in natural images. Both global and local cues are taken into account for localizing text lines in a coarse-to-fine procedure. First, a Fully Convolutional Network (FCN) model is trained for predicting a salient map of text regions in a holistic manner. Then, a set of hypotheses text lines are estimated by combining the salient map and MSER components. Finally, another FCN classifier is used for predicting the centroid of each character, in order to remove the false hypotheses. The framework is general for handling texts in multiple orientations, languages and fonts. The proposed method consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015, and ICDAR2013.
Robust Scene Text Recognition With Automatic Rectification
Authors: Baoguang Shi, Xinggang Wang, Pengyuan Lyu, Cong Yao, Xiang Bai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distortion, curved character placement, etc. We propose RARE (Robust text recognizer with Automatic REctification), a recognition model that is robust to irregular text. RARE is a specially-designed deep neural network, which consists of a Spatial Transformer Network (STN) and a Sequence Recognition Network (SRN). In testing, an image is firstly rectified via a predicted Thin-Plate-Spline (TPS) transformation, into a more "readable" image for the following SRN, which recognizes text through a sequence recognition approach. We show that the model is able to recognize several types of irregular text, including perspective text and curved text. RARE is end-to-end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems. State-of-the-art or highly-competitive performance achieved on several benchmarks well demonstrates the effectiveness of the proposed model.
Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment
Authors: George Trigeorgis, Patrick Snape, Mihalis A. Nicolaou, Epameinondas Antonakos, Stefanos Zafeiriou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem.  Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far.  Specifically, (a) the regressors are learnt independently, (b) the descent directions may cancel one another out and  (c) handcrafted features (e.g., HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.
Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting
Authors: Amin Jourabloo, Xiaoming Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e.g, face recognition and 3D face reconstruction. Recently, there have been a few attempts to solve this problem, but still more research is needed to achieve highly accurate results. In this paper, we propose a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM. We formulate the face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are estimated by a cascade of CNN-based regressors. The dense 3D shape allows us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging databases (AFLW and AFW), with comparison to the state of the art.
Adaptive 3D Face Reconstruction From Unconstrained Photo Collections
Authors: Joseph Roth, Yiying Tong, Xiaoming Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Given a collection of "in-the-wild" face images captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of an individual along with albedo information. Motivated by the success of recent face reconstruction techniques on large photo collections, we extend prior work to adapt to low quality photo collections with fewer images. We achieve this by fitting a 3D Morphable Model to form a personalized template and developing a novel photometric stereo formulation, under a coarse-to-fine scheme. Superior experimental results are reported on synthetic and real-world photo collections.
Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network
Authors: Pavlo Molchanov, Xiaodong Yang, Shalini Gupta, Kihwan Kim, Stephen Tyree, Jan Kautz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three-dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi-modal data. We employ connectionist temporal classification to train the network to predict class labels from in-progress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multi-modal dynamic hand gesture dataset captured with depth, color and stereo-IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83.8%, outperforms competing state-of-the-art algorithms, and approaches human accuracy of 88.4%. Moreover, our method achieves state-of-the-art performance on SKIG and ChaLearn2014 benchmarks.
Kinematic Structure Correspondences via Hypergraph Matching
Authors: Hyung Jin Chang, Tobias Fischer, Maxime Petit, Martina Zambelli, Yiannis Demiris
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.
CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval
Authors: Binod Bhattarai, Gaurav Sharma, Frederic Jurie
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a novel Coupled Projection multi-task Met- ric Learning (CP-mtML) method for large scale face re- trieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more chal- lenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face im- age datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) de- scriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demon- strate the scalability and improved performance of the pro- posed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.
PatchBatch: A Batch Augmented Loss for Optical Flow
Authors: David Gadot, Lior Wolf
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a new pipeline for optical flow computation, based on Deep Learning techniques. We suggest using a Siamese CNN to independently, and in parallel, compute the descriptors of both images. The learned descriptors are then compared efficiently using the L2 norm and do not require network processing of patch pairs. The success of the method is based on an innovative loss function that computes higher moments of the loss distributions for each training batch. Combined with an Approximate Nearest Neighbor patch matching method and a flow interpolation technique, state of the art performance is obtained on the most challenging and competitive optical flow benchmarks.
Joint Recovery of Dense Correspondence and Cosegmentation in Two Images
Authors: Tatsunori Taniai, Sudipta N. Sinha, Yoichi Sato
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a new technique to jointly recover cosegmentation and dense per-pixel correspondence in two images. Our method parameterizes the correspondence field using piecewise similarity transformations and recovers a mapping between the estimated common "foreground" regions in the two images allowing them to be precisely aligned. Our formulation is based on a hierarchical Markov random field model with segmentation and transformation labels. The hierarchical structure uses nested image regions to constrain inference across multiple scales. Unlike prior hierarchical methods which assume that the structure is given, our proposed iterative technique dynamically recovers the structure as a variable along with the labeling. This joint inference is performed in an energy minimization framework using iterated graph cuts. We evaluate our method on a new dataset of 400 image pairs with manually obtained ground truth, where it outperforms state-of-the-art methods designed specifically for either cosegmentation or correspondence estimation.
Multi-View People Tracking via Hierarchical Trajectory Composition
Authors: Yuanlu Xu, Xiaobai Liu, Yang Liu, Song-Chun Zhu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper presents a hierarchical composition approach for multi-view object tracking. The key idea is to adaptively exploit multiple cues in both 2D and 3D, e.g., ground occupancy consistency, appearance similarity, motion coherence etc., which are mutually complementary while tracking the humans of interests over time. While feature online selection has been extensively studied in the past literature, it remains unclear how to effectively schedule these cues for the tracking purpose especially when encountering various challenges, e.g. occlusions, conjunctions, and appearance variations. To do so, we propose a hierarchical composition model and re-formulate multi-view multi-object tracking as a problem of compositional structure optimization. We setup a set of composition criteria, each of which corresponds to one particular cue. The hierarchical composition process is pursued by exploiting different criteria, which impose constraints between a graph node and its offsprings in the hierarchy. We learn the composition criteria using MLE on annotated data and efficiently construct the hierarchical graph by an iterative greedy pursuit algorithm. In the experiments, we demonstrate superior performance of our approach on three public datasets, one of which is newly created by us to test various challenges in multi-view multi-object tracking.
Object Tracking via Dual Linear Structured SVM and Explicit Feature Map
Authors: Jifeng Ning, Jimei Yang, Shaojie Jiang, Lei Zhang, Ming-Hsuan Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Structured support vector machine (SSVM) based methods has demonstrated encouraging performance in recent object tracking benchmarks. However, the complex and expensive optimization limits their deployment in real-world applications. In this paper, we present a simple yet efficient dual linear SSVM (DLSSVM) algorithm to enable fast learning and execution during tracking. By analyzing the dual variables, we propose a primal classifier update formula where the learning step size is computed in closed form. This online learning method significantly improves the robustness of the proposed linear SSVM with low computational cost. Second, we approximate the intersection kernel for feature representations with an explicit feature map to further improve tracking performance. Finally, we extend the proposed DLSSVM tracker in a multiscale manner to address the "drift" problem. Experimental results on large benchmark datasets with 50 and 100 video sequences show that the proposed DLSSVM tracking algorithm achieves state-of-the-art performance.
An Egocentric Look at Video Photographer Identity
Authors: Yedid Hoshen, Shmuel Peleg
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substantial increase in sales every year. As head-worn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed.   We show that camera motion, as can be computed from the egocentric video, provides unique identity information. The photographer can be reliably recognized from a few seconds of video captured when walking. The proposed method achieves more than 90% recognition accuracy in cases where the random success rate is only 3%.  Applications can include theft prevention by locking the camera when not worn by its lawful owner. Searching video sharing services (e.g. YouTube) for egocentric videos shot by a specific photographer may also become possible. An important message in this paper is that photographers should be aware that sharing egocentric video will compromise their anonymity, even when their face is not visible.
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
Authors: Hyeonseob Nam, Bohyung Han
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We propose a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN). Our algorithm pretrains a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation. Our network is composed of shared layers and multiple branches of domain-specific layers, where domains correspond to individual training sequences and each branch is responsible for binary classification to identify target in each domain. We train each domain in the network iteratively to obtain generic target representations in the shared layers. When tracking a target in a new sequence, we construct a new network by combining the shared layers in the pretrained CNN with a new binary classification layer, which is updated online. Online tracking is performed by evaluating the candidate windows randomly sampled around the previous target state. The proposed algorithm illustrates outstanding performance in existing tracking benchmarks.
Hedged Deep Tracking
Authors: Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, Ming-Hsuan Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as the features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the last second layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN trackers into a stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared with several state-of-the-art trackers.
Structural Correlation Filter for Robust Visual Tracking
Authors: Si Liu, Tianzhu Zhang, Xiaochun Cao, Changsheng Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we propose a novel structural correlation filter (SCF) model for robust visual tracking. The proposed SCF model takes part-based tracking strategies into account in a correlation filter tracker, and exploits circular shifts of all parts for their motion modeling to preserve target object structure. Compared with existing correlation filter trackers, our proposed tracker has several advantages: (1) Due to the part strategy, the learned structural correlation filters are less sensitive to partial occlusion, and have computational efficiency and robustness. (2) The learned filters are able to not only distinguish the parts from the background as the traditional correlation filters, but also exploit the intrinsic relationship among local parts via spatial constraints to preserve object structure. (3) The learned correlation filters not only make most parts share similar motion, but also tolerate outlier parts that have different motion. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SCF tracking algorithm performs favorably against several state-of-the-art methods.
Visual Tracking Using Attention-Modulated Disintegration and Integration
Authors: Jongwon Choi, Hyung Jin Chang, Jiyeoup Jeong, Yiannis Demiris, Jin Young Choi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units, and trains multiple elementary trackers in order to modulate the distribution of attention according to various feature and kernel types. In the integration stage it recombines the units to memorize and recognize the target object effectively. With respect to the elementary trackers, we present a novel attentional feature-based correlation filter (AtCF) that focuses on distinctive attentional features. The effectiveness of the proposed algorithm is validated through experimental comparison with state-of-the-art methods on widely-used tracking benchmark datasets.
A Continuous Occlusion Model for Road Scene Understanding
Authors: Vikas Dhiman, Quoc-Huy Tran, Jason J. Corso, Manmohan Chandraker
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling of the reflection and transmission probabilities for the corresponding camera ray. Our modeling is unified in handling occlusions across a variety of scenarios, such as associating structure from motion point tracks with potentially occluded objects or modeling object detection scores in applications such as 3D localization. For point track association, our model uniformly handles static and dynamic objects, which is an advantage over motion segmentation approaches traditionally used in multibody SFM. Detailed experiments on the KITTI dataset show the superiority of the proposed method over both state-of-the-art motion segmentation and a baseline that heuristically uses detection bounding boxes for resolving occlusions. We also demonstrate how our continuous occlusion model may be applied to the task of 3D localization in road scenes.
Virtual Worlds as Proxy for Multi-Object Tracking Analysis
Authors: Adrien Gaidon, Qiao Wang, Yohann Cabon, Eleonora Vig
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling.  In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds.  We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called Virtual KITTI, automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow.  We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance.  As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal.  We show these factors may affect drastically otherwise high-performing deep models for tracking.
Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs
Authors: Keisuke Midorikawa, Toshihiko Yamasaki, Kiyoharu Aizawa
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The uncalibrated photometric stereo problem for non-Lambertian surfaces is challenging because of the large number of unknowns and its ill-posed nature stemming from unknown reflectance functions.  We propose a model that represents various isotropic reflectance functions by using the principal components of items in a dataset, and formulate the uncalibrated photometric stereo as a regression problem.  We then solve it by stepwise optimization utilizing principal components in order of their importance.   We have also developed two techniques that lead to convergence and highly accurate reconstruction, namely (1) a coarse-to-fine approach with normal grouping, and (2) a randomized multipoint search. Our experimental results with synthetic data showed that our method significantly outperformed previous methods. We also evaluated the algorithm in terms of real image data, where it gave good reconstruction results.
Unbiased Photometric Stereo for Colored Surfaces: A Variational Approach
Authors: Yvain Queau, Roberto Mecca, Jean-Denis Durou
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: 3D shape recovery using photometric stereo (PS) gained increasing attention in the computer vision community in the last three decades due to its ability to recover the thinnest geometric structures. Yet, the reliabiliy of PS for color images is difficult to guarantee, because existing methods are usually formulated as the sequential estimation of the colored albedos, the normals and the depth. Hence, the overall reliability depends on that of each subtask. In this work we propose a new formulation of color photometric stereo, based on image ratios, that makes the technique independent from the albedos. This allows the unbiased 3D-reconstruction of colored surfaces in a single step, by solving a system of linear PDEs using a variational approach.
3D Reconstruction of Transparent Objects With Position-Normal Consistency
Authors: Yiming Qian, Minglun Gong, Yee Hong Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction. Under the assumption that the rays refract only twice when traveling through the object, we present the first approach to simultaneously reconstructing the 3D positions and normals of the object's surface at both refraction locations. Our acquisition setup requires only two cameras and one monitor, which serves as the light source. After acquiring the ray-ray correspondences between each camera and the monitor, we solve an optimization function which enforces a new position-normal consistency constraint. That is, the 3D positions of surface points shall agree with the normals required to refract the rays under Snell's law. Experimental results using both synthetic and real data demonstrate the robustness and accuracy of the proposed approach.
Real-Time Depth Refinement for Specular Objects
Authors: Roy Or-El, Rom Hershkovitz, Aaron Wetzler, Guy Rosman, Alfred M. Bruckstein, Ron Kimmel
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research. Yet, the precision of existing depth scanners is not accurate enough to recover fine details of a scanned object. While modern shading based depth refinement methods have been proven to work well with Lambertian objects, they break down in the presence of specularities. We present a novel shape from shading framework that addresses this issue and enhances both diffuse and specular objects' depth profiles. We take advantage of the built-in monochromatic IR projector and IR images of the RGB-D scanners and present a lighting model that accounts for the specular regions in the input image. Using this model, we reconstruct the depth map in real-time. Both quantitative tests and visual evaluations prove that the proposed method produces state of the art depth reconstruction results.
Recovering Transparent Shape From Time-Of-Flight Distortion
Authors: Kenichiro Tanaka, Yasuhiro Mukaigawa, Hiroyuki Kubo, Yasuyuki Matsushita, Yasushi Yagi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: This paper presents a method for recovering shape and normal of a transparent object from a single viewpoint using a Time-of-Flight (ToF) camera. Our method is built upon the fact that the speed of light varies with the refractive index of the medium and therefore the depth measurement of a transparent object with a ToF camera may be distorted. We show that, from this ToF distortion, the refractive light path can be uniquely determined by estimating a single parameter. We estimate this parameter by introducing a surface normal consistency between the one determined by a light path candidate and the other computed from the corresponding shape. The proposed method is evaluated by both simulation and real-world experiments and shows faithful transparent shape recovery.
Robust Light Field Depth Estimation for Noisy Scene With Occlusion
Authors: W. Williem, In Kyu Park
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Light field depth estimation is an essential part of many light field applications. Numerous algorithms have been developed using various light field characteristics. However, conventional methods fail when handling noisy scene with occlusion. To remedy this problem, we present a light field depth estimation method which is more robust to occlusion and less sensitive to noise. Novel data costs using angular entropy metric and adaptive defocus response are introduced. Integration of both data costs improves the occlusion and noise invariant capability significantly. Cost volume filtering and graph cut optimization are utilized to improve the accuracy of the depth map. Experimental results confirm that the proposed method is robust and achieves high quality depth maps in various scenes. The proposed method outperforms the state-of-the-art light field depth estimation methods in qualitative and quantitative evaluation.
Rotational Crossed-Slit Light Field
Authors: Nianyi Li, Haiting Lin, Bilin Sun, Mingyuan Zhou, Jingyi Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Light fields (LFs) are image-based representation that records the radiance along all rays along every direction through every point in space. Traditionally LFs are acquired by using a 2D grid of evenly spaced pinhole cameras or by translating a pinhole camera along the 2D grid using a robot arm. In this paper, we present a novel LF sampling scheme by exploiting a special non-centric camera called the crossed-slit or XSlit camera. An XSlit camera acquires rays that simultaneously pass through two oblique slits. We show that, instead of translating the camera as in the pinhole case, we can effectively sample the LF by rotating individual or both slits while keeping the camera fixed. This leads a "fixed-location" LF acquisition scheme. We further show through theoretical analysis and experiments that the resulting XSlit LFs provide several advantages: they provide more dense spatial-angular sampling, are amenable multi-view stereo matching and volumetric reconstruction, and can synthesize unique refocusing effects.
Single Image Object Modeling Based on BRDF and R-Surfaces Learning
Authors: Fabrizio Natola, Valsamis Ntouskos, Fiora Pirri, Marta Sanzari
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: A methodology for 3D surface modeling from a single image is proposed.  The principal novelty is concave and specular surface modeling without any externally imposed prior. The main idea of the method is to use BRDFs and generated rendered surfaces, to transfer the normal field, computed for the generated samples, to the unknown surface.  The transferred information is adequate to blow and sculpt the segmented image mask in to a bas-relief of the object. The object surface is further refined basing on a  photo-consistency formulation that relates for error minimization the original image and the modeled object.
A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis
Authors: Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, Michael S. Okun, David E. Viallancourt, Baba C. Vemuri
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Regression is an essential tool in Statistical analysis of data with many applications in Computer Vision, Machine Learning, Medical Imaging and various disciplines of Science and Engineering. Linear and nonlinear regression in a vector space setting has been well studied in literature. However, generalizations to manifold-valued data are only recently gaining popularity. With the exception of a few, most existing methods of regression for manifold valued data are limited to geodesic regression which is a generalization of the linear regression in vector-spaces. In this paper, we present a novel nonlinear kernel-based regression method that is applicable to manifold valued data. Our method is applicable to cases when the independent and dependent variables in the regression model are both manifold-valued or one is manifold-valued and the other is vector or scalar valued. Further, unlike most methods, our method does not require any imposed ordering on the manifold-valued data. The performance of our model is tested on a large number of real data sets acquired from Alzhiemers and movement disorder (Parkinsons and Essential Tremor) patients. We present an extensive set of results along with statistical validation and comparisons.
RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition
Authors: Qilong Wang, Peihua Li, Wangmeng Zuo, Lei Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts. In this paper, we propose a novel image descriptor, namely, robust approximate infinite dimensional Gaussian (RAID-G). The challenges of RAID-G mainly lie on two aspects: (1) description of infinite dimensional Gaussian is difficult due to its non-linear Riemannian geometric structure and the infinite dimensional setting, hence effective approximation is necessary; (2) traditional maximum likelihood estimation (MLE) is not robust to high (even infinite) dimensional covariance matrix in Gaussian setting. To address these challenges, explicit feature mapping (EFM) is first introduced for effective approximation of infinite dimensional Gaussian induced by additive kernel function, and then a new regularized MLE method based on von Neumann divergence is proposed for robust estimation of covariance matrix. The EFM and proposed regularized MLE allow a closed-form of RAID-G, which is very efficient and effective for high dimensional features. We extend RAID-G by using the outputs of deep convolutional neural networks as original features, and apply it to material recognition. Our approach is evaluated on five material benchmarks and one fine-grained benchmark. It achieves 84.9% accuracy on FMD and 86.3% accuracy on UIUC material database, which are much higher than state-of-the-arts.
An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability
Authors: Nikolaos Karianakis, Jingming Dong, Stefano Soatto
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: We conduct an empirical study to test the ability of convolutional neural networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. In theory, averaging the latter should yield inferior performance compared to proper marginalization. Yet empirical evidence suggests the converse, leading us to conclude that - at the current level of complexity of convolutional architectures and scale of the data sets used to train them - CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on a classification task using the ImageNet Challenge benchmark and on a wide-baseline matching task using the Oxford and Fischer's datasets.
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Authors: Varun Jampani, Martin Kiefel, Peter V. Gehler
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Bilateral filters have wide spread use due to their edge-preserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNN and propose bilateral neural networks for the use of high-dimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters.
Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis
Authors: Fujiao Ju, Yanfeng Sun, Junbin Gao, Simeng Liu, Yongli Hu, Baocai Yin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: The probabilistic principal component analysis (PPCA) is built upon a global linear mapping, with which it is insufficient to model complex data variation. This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data. With multi-components in the mixture, this model can be seen as a `soft' cluster algorithm and has capability of modeling data with complex structures. A Bayesian inference scheme has been proposed based on the variational EM (Expectation-Maximization) approach for learning model parameters.   Experiments on some publicly available databases show that the performance of mixB2DPPCA has been largely improved, resulting in more accurate reconstruction errors and recognition rates than the existing PCA-based algorithms.
Rolling Rotations for Recognizing Human Actions From 3D Skeletal Data
Authors: Raviteja Vemulapalli, Rama Chellapa
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Recently, skeleton-based human action recognition has been receiving significant attention from various research communities due to the availability of depth sensors and real-time depth-based 3D skeleton estimation algorithms. In this work, we use rolling maps for recognizing human actions from 3D skeletal data. The rolling map is a well-defined mathematical concept that has not been explored much by the vision community. First, we represent each skeleton using the relative 3D rotations between various body parts. Since 3D rotations are members of the special orthogonal group SO(3), our skeletal representation becomes a point in the Lie group SO(3) X ... X SO(3), which is also a Riemannian manifold. Then, using this representation, we model human actions as curves in this Lie group. Since classification of curves in this non-Euclidean space is a difficult task, we unwrap the action curves onto the Lie algebra (which is a vector space) by combining the logarithm map with rolling maps, and perform classification in the Lie algebra. Experimental results on three action datasets show that the proposed approach performs equally well or better when compared to state-of-the-art.
Improving the Robustness of Deep Neural Networks via Stability Training
Authors: Stephan Zheng, Yang Song, Thomas Leung, Ian Goodfellow
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such as compression, rescaling, and cropping. We validate our method by stabilizing the state-of-the-art Inception architecture against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on large-scale near-duplicate detection, similar-image ranking, and classification on noisy datasets.
Logistic Boosting Regression for Label Distribution Learning
Authors: Chao Xing, Xin Geng, Hui Xue
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: Label Distribution Learning (LDL) is a general learning framework which includes both single label and multi-label learning as its special cases. One of the main assumptions made in traditional LDL algorithms is the derivation of the parametric model as the maximum entropy model. While it is a reasonable assumption without additional information, there is no particular evidence supporting it in the problem of LDL. Alternatively, using a general LDL model family to approximate this parametric model can avoid the potential influence of the specific model. In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint. For each step, we can fit individual weighted regression function (base learner) to realize the optimization gradually. The base learners are chosen as weighted regression tree and vector tree, which constitute two algorithms named LDLogitBoost and AOSO-LDLogitBoost in this paper. Experiments on facial expression recognition, crowd opinion prediction on movies and apparent age estimation show that LDLogitBoost and AOSO-LDLogitBoost can achieve better performance than traditional LDL algorithms as well as other LogitBoost algorithms.
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold
Authors: Xikang Zhang, Yin Wang, Mengran Gou, Mario Sznaier, Octavia Camps
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (Main Conference Track), 2016

Abstract: In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the  sequences into  a Riemannian manifold by  using positive definite regularized Gram matrices of  their Hankelets.  The  advantages of the this approach are: 1) it allows for using non-Euclidean similarity functions  on the Positive Definite matrix manifold, which capture better the underlying geometry than directly comparing the sequences or their  Hankel matrices; and 2)  Gram matrices inherit desirable properties from the underlying Hankel matrices: their rank  measure the complexity of the underlying dynamics, and the rank and the coefficients of the associated regressive models are  invariant to affine transformations and varying initial conditions. The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences. In spite of its simplicity,  the  performance of this approach is competitive or  better  than  using state-of-art approaches for this problem.  Further, these results hold across a variety of  metrics, supporting the idea that the improvement stems from the embedding itself, rather than from  using  one of these metrics.