mtybilly/Instruct2DS huggingface dataset:
Exact Bias Correction and Covariance Estimation for Stereo Vision
Authors: Charles Freundlich, Michael Zavlanos, Philippos Mordohai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig. Our analysis is based on the observation that, due to quantization error, a 3D point reconstructed by triangulation essentially represents an entire region in space.  The true location of the world point that generated the triangulated point could be anywhere in this region.  We argue that the reconstructed point, if it is to represent this region in space without bias, should be located at the centroid of this region, which is not what has been done in the literature.  We derive the exact geometry of these regions in space, which we call 3D cells, and we show how they can be viewed as uniform distributions of possible pre-images of the pair of corresponding pixels. By assuming a uniform distribution of points in 3D, as opposed to a uniform distribution of the projections of these 3D points on the images, we arrive at a fast and exact computation of the triangulation bias in each cell. In addition, we derive the exact covariance matrices of the 3D cells. We validate our approach in a variety of simulations ranging from 3D reconstruction to camera localization and relative motion estimation. In all cases, we are able to demonstrate a marked improvement compared to conventional techniques for small disparity values, for which bias is significant and the required corrections are large.
Computing Similarity Transformations From Only Image Correspondences
Authors: Chris Sweeney, Laurent Kneip, Tobias Hollerer, Matthew Turk
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose a novel solution for computing the relative pose between two generalized cameras that includes reconciling the internal scale of the generalized cameras. This approach can be used to compute a similarity transformation between two coordinate systems, making it useful for loop closure in visual odometry and registering multiple structure from motion reconstructions together. In contrast to alternative similarity transformation methods, our approach uses 2D-2D image correspondences thus is not subject to the depth uncertainty that often arises with 3D points. We utilize a known vertical direction (which may be easily obtained from IMU data or vertical vanishing point detection) of the generalized cameras to solve the generalized relative pose and scale problem as an efficient Quadratic Eigenvalue Problem. To our knowledge, this is the first method for computing similarity transformations that does not require any 3D information. Our experiments on synthetic and real data demonstrate that this leads to improved performance compared to methods that use 3D-3D or 2D-3D correspondences, especially as the depth of the scene increases.
Image Segmentation in Twenty Questions
Authors: Christian Rupprecht, Loic Peter, Nassir Navab
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Consider the following scenario between a human user and the computer. Given an image, the user thinks of an object to be segmented within this picture, but is only allowed to provide binary inputs to the computer (yes or no). In these conditions, can the computer guess this hidden segmentation by asking well-chosen questions to the user? We introduce a strategy for the computer to increase the accuracy of its guess in a minimal number of questions. At each turn, the current belief about the answer is encoded in a Bayesian fashion via a probability distribution over the set of all possible segmentations. To efficiently handle this huge space, the distribution is approximated by sampling representative segmentations using an adapted version of the Metropolis-Hastings algorithm, whose proposal moves build on a geodesic distance transform segmentation method. Following a dichotomic search, the question halving the weighted set of samples is finally picked, and the provided answer is used to update the belief for the upcoming rounds. The performance of this strategy is assessed on three publicly available datasets with diverse visual properties. Our approach shows to be a tractable and very adaptive solution to this problem.
Interaction Part Mining: A Mid-Level Approach for Fine-Grained Action Recognition
Authors: Yang Zhou, Bingbing Ni, Richang Hong, Meng Wang, Qi Tian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Modeling human-object interactions and manipulating motions lies in the heart of fine-grained action recognition. Previous methods heavily rely on explicit detection of the object being interacted, which requires intensive human labour on object annotation. To bypass this constraint and achieve better classification performance, in this work, we propose a novel fine-grained action recognition pipeline by interaction part proposal and discriminative mid-level part mining. Firstly, we generate a large number of candidate object regions using off-the-shelf object proposal tool, e.g., BING. Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them. We then propose an efficient approximate graph segmentation algorithm to partition and filter the graph into consistent local dense sub-graphs. These sub-graphs, which are spatio-temporal sub-volumes, represent our candidate interaction parts. Finally, we mine discriminative mid-level part detectors from the features computed over the candidate interaction parts. Bag-of-detection scores based on a novel Max-N pooling scheme are computed as the action representation for a video sample. We conduct extensive experiments on human-object interaction datasets including MPII Cooking and MSR Daily Activity 3D. The experimental results demonstrate that the proposed framework achieves consistent improvements over the state-of-the-art action recognition accuracies on the benchmarks, without using any object annotation.
Sparse Projections for High-Dimensional Binary Codes
Authors: Yan Xia, Kaiming He, Pushmeet Kohli, Jian Sun
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper addresses the problem of learning long binary codes from high-dimensional data. We observe that two key challenges arise while learning and using long binary codes: (1) lack of an effective regularizer for the learned high-dimensional mapping and (2) high computational cost for computing long codes. In this paper, we overcome both these problems by introducing a sparsity encouraging regularizer that reduces the effective number of parameters involved in the learned projection operator. This regularizer not only reduces overfitting but, due to the sparse nature of the projection matrix, also leads to a dramatic reduction in the computational cost. To evaluate the effectiveness of our method, we analyze its performance on the problems of nearest neighbour search, image retrieval and image classification. Experiments on a number of challenging datasets show that our method leads to better accuracy than dense projections (ITQ and LSH) with the same code lengths, and meanwhile is over an order of magnitude faster. Furthermore, our method is also more accurate and faster than other recently proposed methods for speeding up high-dimensional binary encoding.
Hierarchically-Constrained Optical Flow
Authors: Ryan Kennedy, Camillo J. Taylor
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper presents a novel approach to solving optical flow problems using a discrete, tree-structured MRF derived from a hierarchical segmentation of the image. Our method can be used to find globally optimal matching solutions even for problems involving very large motions. Experiments demonstrate that our approach is competitive on the MPI-Sintel dataset and that it can significantly outperform existing methods on problems involving large motions.
The k-Support Norm and Convex Envelopes of Cardinality and Rank
Authors: Anders Eriksson, Trung Thanh Pham, Tat-Jun Chin, Ian Reid
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Sparsity, or cardinality, as a tool for feature selection is extremely common in a vast number of current computer vision applications. The $k$-support norm is a recently proposed norm with the proven property of providing the tightest convex bound on cardinality over the Euclidean norm unit ball. In this paper we present a re-derivation of this norm, with the hope of shedding further light on this particular surrogate function. In addition, we also present a connection between the rank operator, the nuclear norm and the $k$-support norm. Finally, based on the results established in this re-derivation, we propose a novel algorithm with significantly improved computational efficiency, empirically validated on a number of different problems, using both synthetic and real world data.
Recurrent Convolutional Neural Network for Object Recognition
Authors: Ming Liang, Xiaolin Hu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In recent years, the convolutional neural network (CNN) has achieved great success in many computer vision tasks. Partially inspired by neuroscience, CNN shares many properties with the visual system of the brain. A prominent difference is that CNN is typically a feed-forward architecture while in the visual system recurrent connections are abundant. Inspired by this fact, we propose a recurrent CNN (RCNN) for object recognition by incorporating recurrent connections into each convolutional layer. Though the input is static, the activities of RCNN units evolve over time so that the activity of each unit is modulated by the activities of its neighboring units. This property enhances the ability of the model to integrate the context information, which is important for object recognition. Like other recurrent neural networks, unfolding the RCNN through time can result in an arbitrarily deep network with a fixed number of parameters. Furthermore, the unfolded network has multiple paths, which can facilitate the learning process. The model is tested on four benchmark object recognition datasets: CIFAR-10, CIFAR-100, MNIST and SVHN. With fewer trainable parameters, RCNN outperforms the state-of-the-art models on all of these datasets. Increasing the number of parameters leads to even better performance. These results demonstrate the advantage of the recurrent structure over purely feed-forward structure for object recognition.
Feedforward Semantic Segmentation With Zoom-Out Features
Authors: Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We introduce a purely feed-forward architecture for semantic segmentation. We map small image elements (superpixels) to rich feature representations extracted from a sequence of nested regions of increasing extent. These regions are obtained by "zooming out" from the superpixel all the way to scene-level resolution. This approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead superpixels are classified by a feedforward multilayer network. Our architecture achieves 69.6% average accuracy on the PASCAL VOC 2012 test set, and 86.1 pixel accuracy on Stanford Background Dataset.
The Aperture Problem for Refractive Motion
Authors: Tianfan Xue, Hossein Mobahi, Fredo Durand, William T. Freeman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: When viewed through a small aperture, a moving image provides incomplete information about the local motion. Only the component of motion along the local image gradient is constrained. In an essential part of optical flow algorithms, information must be aggregated from nearby image locations in order to estimate all components of motion. This limitation of local evidence for estimating optical flow is called ''the aperture problem''.  We pose and solve a generalization of the aperture problem for moving refractive elements. We consider a common setup in air flow imaging or telescope observation: a camera is viewing a static background, and an unknown refractive elements undergoing unknown motion between them. Then we are addressing this fundamental question: what does the local image motion tell us about the motion of refractive elements?  We show that the information gleaned through a local aperture for this case is very different than that for optical flow. In optical flow, the movement of 1D structure already constrains the motion in a certain direction. However, we cannot infer any information about the refractive motion from the movement of 1D structure in the observed sequence, and can only recover one component of the motion from 2D structure. Results on both simulated and real sequences are shown to illustrate our theory.
Saliency-Aware Geodesic Video Object Segmentation
Authors: Wenguan Wang, Jianbing Shen, Fatih Porikli
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We introduce an unsupervised, geodesic distance based, salient video object segmentation method. Unlike traditional methods, our method incorporates saliency as prior for object via the computation of robust geodesic measurement. We consider two discriminative visual features: spatial edges and temporal motion boundaries as indicators of foreground object locations. We first generate frame-wise spatiotemporal saliency maps using geodesic distance from these indicators. Building on the observation that foreground areas are surrounded by the regions with high spatiotemporal edge values, geodesic distance provides an initial estimation for foreground and background. Then, high-quality saliency results are produced via the geodesic distances to background regions in the subsequent frames. Through the resulting saliency maps, we build global appearance models for foreground and background. By imposing motion continuity, we establish a dynamic location model for each frame. Finally, the spatiotemporal saliency maps, appearance models and dynamic location models are combined into an energy minimization framework to attain both spatially and temporally coherent object segmentation. Extensive quantitative and qualitative experiments on benchmark video dataset demonstrate the superiority of the proposed method over the state-of-the-art algorithms.
DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets
Authors: Sukrit Shankar, Vikas K. Garg, Roberto Cipolla
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines.  For instance, given a noun (say forest) and its associated attributes (say dense, sunlit, autumn), search engines can now generate many valid images for any attribute-noun pair (dense forests, autumn forests, etc). However, images for an attribute-noun pair do not contain any information about other attributes (like which forests in the autumn are dense too). Thus, a weakly supervised scenario occurs: each of the M attributes corresponds to a class such that a training image in class m = 1,...,M contains a single label that indicates the presence of the m(th) attribute only. The task is to discover all the attributes present in a test image.   Deep Convolutional Neural Networks (CNNs) have enjoyed remarkable success in vision applications recently.  However, in a weakly supervised scenario, widely used CNN training procedures do not learn a robust model for predicting multiple attribute labels simultaneously. The primary reason is that the attributes highly co-occur within the training data, and unlike objects, do not generally exist as well-defined spatial boundaries within the image. To ameliorate this limitation, we propose Deep-Carving, a novel training procedure with CNNs, that helps the net efficiently carve itself for the task of multiple attribute prediction. During training, the responses of the feature maps are exploited in an ingenious way to provide the net with multiple pseudo-labels (for training images) for subsequent iterations. The process is repeated periodically after a fixed number of iterations, and enables the net carve itself iteratively for efficiently disentangling features.  Additionally, we contribute a noun-adjective pairing inspired Natural Scenes Attributes Dataset to the research community, CAMIT - NSAD, containing a number of co-occurring attributes within a noun category. We describe, in detail, salient aspects of this dataset.  Our experiments on CAMIT-NSAD and the SUN Attributes Dataset, with weak supervision, clearly demonstrate that the Deep-Carved CNNs consistently achieve considerable improvement in the precision of attribute prediction over popular baseline methods.
Rent3D: Floor-Plan Priors for Monocular Layout Estimation
Authors: Chenxi Liu, Alexander G. Schwing, Kaustav Kundu, Raquel Urtasun, Sanja Fidler
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: The goal of this paper is to enable a 3D "virtual-tour" of an apartment given a small set of monocular images of different rooms, as well as a 2D floor plan. We frame the problem as inference in a Markov Random Field which reasons about the layout of each room and its relative pose (3D rotation and translation) within the full apartment. This gives us accurate camera pose in the apartment for each image. What sets us apart from past work in layout estimation is the use of floor plans as a source of prior knowledge, as well as localization of each image within a bigger space (apartment). In particular, we exploit the floor plan to impose aspect ratio constraints across the layouts of different rooms, as well as to extract semantic information, e.g., the location of windows which are marked in floor plans. We show that this information can significantly help in resolving the challenging room-apartment alignment problem. We also derive an efficient exact inference algorithm which takes only a few ms per apartment. This is due to the fact that we exploit integral geometry as well as our new bounds on the aspect ratio of rooms which allow us to carve the space, significantly reducing the number of physically possible configurations. We demonstrate the effectiveness of our approach on a new dataset which contains over 200 apartments.
Learning a Sequential Search for Landmarks
Authors: Saurabh Singh, Derek Hoiem, David Forsyth
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose a general method to find landmarks in images of objects using both appearance and spatial context. This method is applied without changes to two problems: parsing human body layouts, and finding landmarks in images of birds. Our method learns a sequential search for localizing landmarks, iteratively detecting new landmarks given the appearance and contextual information from the already detected ones. The choice of landmark to be added is opportunistic and depends on the image; for example, in one image a head-shoulder group might be expanded to a head-shoulder-hip group but in a different image to a head-shoulder-elbow group. The choice of initial landmark is similarly image dependent.  Groups are scored using a learned function, which is used to expand them greedily. Our scoring function is learned from data labelled with landmarks but without any labeling of a detection order.  Our method represents a novel spatial model for the kinematics of groups of landmarks, and displays strong performance on two different model problems.
Fully Convolutional Networks for Semantic Segmentation
Authors: Jonathan Long, Evan Shelhamer, Trevor Darrell
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation.  Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.  We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models.  We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task.  We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations.  Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.
Deep Correlation for Matching Images and Text
Authors: Fei Yan, Krystian Mikolajczyk
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper addresses the problem of matching images and captions in a joint latent space learnt with deep canonical correlation analysis (DCCA). The image  and caption data are represented by the  outputs of  the vision and text based deep neural networks. The high dimensionality of the features presents a great challenge in terms of memory and speed complexity when used in DCCA framework. We address these problems by a GPU implementation and propose methods to deal with overfitting. This makes it possible to evaluate DCCA approach on popular caption-image matching benchmarks. We compare our approach to other recently proposed techniques and present state of the art results on three datasets.
Multi-Objective Convolutional Learning for Face Labeling
Authors: Sifei Liu, Jimei Yang, Chang Huang, Ming-Hsuan Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm, and accurate labeling results on challenging images can be obtained by the proposed algorithm for real-world applications.
Deep Multiple Instance Learning for Image Classification and Auto-Annotation
Authors: Jiajun Wu, Yinan Yu, Chang Huang, Kai Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: The recent development in learning deep representations has demonstrated its wide applications in traditional vision tasks like classification and detection. However, there has been little investigation on how we could build up a deep learning framework in a weakly supervised setting. In this paper, we attempt to model deep learning in a weakly supervised learning (multiple instance learning) framework. In our setting, each image follows a dual multi-instance assumption, where its object proposals and possible text annotations can be regarded as two instance sets. We thus design effective systems to exploit the MIL property with deep learning strategies from the two ends; we also try to jointly learn the relationship between object and annotation proposals. We conduct extensive experiments and prove that our weakly supervised deep learning framework not only achieves convincing performance in vision tasks including classification and image annotation, but also extracts reasonable region-keyword pairs with little supervision, on both widely used benchmarks like PASCAL VOC and MIT Indoor Scene 67, and also a dataset for image- and patch-level annotations.
Multi-Instance Object Segmentation With Occlusion Handling
Authors: Yi-Ting Chen, Xiaokai Liu, Ming-Hsuan Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottom-up schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012 segmentation set. The proposed algorithm achieves favorable results on the joint detection and segmentation task against the state-of-the-art method both quantitatively and qualitatively.
Material Recognition in the Wild With the Materials in Context Database
Authors: Sean Bell, Paul Upchurch, Noah Snavely, Kavita Bala
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In this paper, we introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the wild.  MINC is an order of magnitude larger than previous material databases, while being more diverse and well-sampled across its 23 categories. Using MINC, we train convolutional neural networks (CNNs) for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. For patch-based classification on MINC we found that the best performing CNN architectures can achieve 85.2% mean class accuracy. We convert these trained CNN classifiers into an efficient fully convolutional framework combined with a fully connected conditional random field (CRF) to predict the material at every pixel in an image, achieving 73.1% mean class accuracy. Our experiments demonstrate that having a large, well-sampled dataset such as MINC is crucial for real-world material recognition and segmentation.
Understanding Pedestrian Behaviors From Stationary Crowd Groups
Authors: Shuai Yi, Hongsheng Li, Xiaogang Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Pedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance. Stationary crowd groups are a key factor influencing pedestrian walking patterns but was largely ignored in literature. In this paper, a novel model is proposed for pedestrian behavior modeling by including stationary crowd groups as a key component. Through inference on the interactions between stationary crowd groups and pedestrians, our model can be used to investigate pedestrian behaviors. The effectiveness of the proposed model is demonstrated through multiple applications, including walking path prediction, destination prediction, personality classification, and abnormal event detection. To evaluate our model, a large pedestrian walking route dataset is built. The walking routes of 12, 684 pedestrians from a one-hour crowd surveillance video are manually annotated. It will be released to the public and benefit future research on pedestrian behavior analysis and crowd scene understanding.
Depth From Focus With Your Mobile Phone
Authors: Supasorn Suwajanakorn, Carlos Hernandez, Steven M. Seitz
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: While prior depth from focus and defocus techniques operated on laboratory scenes, we introduce the first depth from focus (DfF) method capable of handling images from mobile phones and other hand-held cameras. Achieving this goal requires solving a novel uncalibrated DfF problem and aligning the frames to account for scene parallax. Our approach is demonstrated on a range of challenging cases and produces high quality results.
Fusion Moves for Correlation Clustering
Authors: Thorsten Beier, Fred A. Hamprecht, Jorg H. Kappes
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Correlation clustering, or multicut partitioning, is widely used in image segmentation for partitioning an undirected graph or image with positive and negative edge weights  such that the sum of cut edge weights is minimized. Due to its NP-hardness, exact solvers do not scale and approximative solvers often give unsatisfactory results. We investigate scalable methods for correlation clustering. To this end we define fusion moves for the correlation clustering problem. Our algorithm iteratively fuses the current and a proposed partitioning which  monotonously improves the partitioning and maintains a valid partitioning at all times. Furthermore, it scales to larger datasets, gives near optimal solutions, and at the same time shows a good anytime performance.
Second-Order Constrained Parametric Proposals and Sequential Search-Based Structured Prediction for Semantic Segmentation in RGB-D Images
Authors: Dan Banica, Cristian Sminchisescu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many visual categories and instances. Our approach is based on a parametric figure-ground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorithm that produces a complete scene estimate. Our contributions can be summarized as follows: (1) a generalization of parametric max flow figure-ground proposal methodology to take advantage of intensity and depth information, in order to systematically and efficiently generate the breakpoints of an underlying spatial model in polynomial time, (2) new region description methods based on second-order pooling over multiple features constructed using both intensity and depth channels, (3) a principled search-based structured prediction inference and learning process that resolves conflicts in overlapping spatial partitions and selects regions sequentially towards complete scene estimates, and (4) extensive evaluation of the impact of depth, as well as the effectiveness of a large number of descriptors, both pre-designed and automatically obtained using deep learning, in a difficult RGB-D semantic segmentation problem with 92 classes. We report state of the art results in the challenging NYU Depth Dataset V2, extended for the RMRC 2013 and RMRC 2014 Indoor Segmentation Challenges, where currently the proposed model ranks first. Moreover, we show that by combining second-order and deep learning features, over 15% relative accuracy improvements can be additionally achieved. In a scene classification benchmark, our methodology further improves the state of the art by 24%.
Metric Imitation by Manifold Transfer for Efficient Vision Applications
Authors: Dengxin Dai, Till Kroeger, Radu Timofte, Luc Van Gool
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Metric learning has proved very  successful. However, human annotations are necessary. In this paper, we propose an unsupervised method, dubbed Metric Imitation (MI), where metrics over one cheap feature (target features, TFs) are learned by imitating the standard metrics over another sophisticated, off-the-shelf feature (source features, SFs) by transferring the view-independent property manifold structures. In particular, MI consists of: 1) quantifying the properties of source metrics as manifold geometry, 2) transferring the manifold from source domain to target domain, and 3) learning a mapping of TFs so that the manifold is approximated as well as possible in the mapped feature domain. MI is useful in at least two scenarios where: 1) TFs are more efficient computationally and in terms of memory than SFs; and 2) SFs contain privileged information, but they are not available during testing. For the former, MI is evaluated on image clustering, category-based image retrieval, and instance-based object retrieval, with three SFs and three TFs. For the latter, MI is tested on the task of example-based image super-resolution, where high-resolution patches are taken as SFs and low-resolution patches as TFs. Experiments show that MI is able to provide good metrics while avoiding expensive data labeling efforts and that it achieves state-of-the-art performance for image super-resolution. In addition, manifold transfer is an interesting direction of transfer learning.
The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose
Authors: Silvia Zuffi, Michael J. Black
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose a new 3D model of the human body that is both realistic and part-based. The body is represented by a graphical model in which nodes of the graph correspond to body parts that can independently translate and rotate in 3D and deform to represent different body shapes and to capture pose-dependent shape variations. Pairwise potentials define a "stitching cost" for pulling the limbs apart, giving rise to  the Stitched Puppet (SP) model. Unlike existing realistic 3D body models, the distributed representation facilitates inference by allowing the model to more effectively explore the space of poses, much like existing 2D pictorial structures models. We infer pose and body shape using a form of particle-based max-product belief propagation. This gives SP the realism of recent 3D body models with the computational advantages of part-based models. We apply SP to two challenging problems involving estimating human shape and pose from 3D data. The first is the FAUST mesh alignment challenge, where ours is the first method to successfully align all 3D meshes with no pose prior. The second involves estimating pose and shape from crude visual hull representations of complex body movements.
Scene Labeling With LSTM Recurrent Neural Networks
Authors: Wonmin Byeon, Thomas M. Breuel, Federico Raue, Marcus Liwicki
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification. We investigate two-dimensional (2D) LSTM networks for natural scene images taking into account the complex spatial dependencies of labels. Prior methods generally have required separate classification and image segmentation stages and/or pre- and post-processing. In our approach, classification, segmentation, and context integration are all carried out by 2D LSTM networks, allowing texture and spatial model parameters to be learned within a single model. The networks efficiently capture local and global contextual information over raw RGB values and adapt well for complex scene images. Our approach, which has a much lower computational complexity than prior methods, achieved state-of-the-art performance over the Stanford Background and the SIFT Flow datasets. In fact, if no pre- or post-processing is applied, LSTM networks outperform other state-of-the-art approaches. Hence, only with a single-core Central Processing Unit (CPU), the running time of our approach is equivalent or better than the compared state-of-the-art approaches which use a Graphics Processing Unit (GPU). Finally, our networks' ability to visualize feature maps from each layer supports the hypothesis that LSTM networks are overall suited for image processing tasks.
FAemb: A Function Approximation-Based Embedding Method for Image Retrieval
Authors: Thanh-Toan Do, Quang D. Tran, Ngai-Man Cheung
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: The objective of this paper is to design an embedding method mapping local features describing image (e.g. SIFT) to a higher dimensional representation used for image retrieval problem. By investigating the relationship between the linear approximation of a nonlinear function in high dimensional space and state-of-the-art feature representation used in image retrieval, i.e., VLAD, we first introduce a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation used in the image retrieval framework. The evaluation shows that our embedding method gives a performance boost over the state of the art in image retrieval, as demonstrated by our experiments on the standard public image retrieval benchmarks.
Automatically Discovering Local Visual Material Attributes
Authors: Gabriel Schwartz, Ko Nishino
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Shape cues play an important role in computer vision, but shape is not the only information available in images. Materials, such as fabric and plastic, are discernible in images even when shapes, such as those of an object, are not. We argue that it would be ideal to recognize materials without relying on object cues such as shape. This would allow us to use materials as a context for other vision tasks, such as object recognition. Humans are intuitively able to find visual cues that describe materials. While previous frameworks attempt to recognize these cues (as visual material traits), they rely on a fully-supervised set of training image patches. This requirement is not feasible when multiple annotators and large quantities of images are involved. In this paper, we derive a framework that allows us to discover locally-recognizable material attributes from crowdsourced perceptual material distances. We show that the attributes we discover do in fact separate material categories. Our learned attributes exhibit the same desirable properties as material traits, despite the fact that they are discovered using only partial supervision.
Depth Image Enhancement Using Local Tangent Plane Approximations
Authors: Kiyoshi Matsuo, Yoshimitsu Aoki
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper describes a depth image enhancement method for consumer RGB-D cameras. Most existing methods use the pixel-coordinates of the aligned color image. Because the image plane generally has no relationship to the measured surfaces, the global coordinate system is not suitable to handle their local geometries. To improve enhancement accuracy, we use local tangent planes as local coordinates for the measured surfaces. Our method is composed of two steps, a calculation of the local tangents and surface reconstruction. To accurately estimate the local tangents, we propose a color heuristic calculation and an orientation correction using their positional relationships. Additionally, we propose a surface reconstruction method by ray-tracing to local tangents. In our method, accurate depth image enhancement is achieved by using the local geometries approximated by the local tangents. We demonstrate the effectiveness of our method using synthetic and real sensor data. Our method has a high completion rate and achieves the lowest errors in noisy cases when compared with existing techniques.
Video Co-Summarization: Video Summarization by Visual Co-Occurrence
Authors: Wen-Sheng Chu, Yale Song, Alejandro Jaimes
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present video co-summarization, a novel perspective to video summarization that exploits visual co-occurrence across multiple videos. Motivated by the observation that important visual concepts tend to appear repeatedly across videos of the same topic, we propose to summarize a video by finding shots that co-occur most frequently across videos collected using a topic keyword. The main technical challenge is dealing with the sparsity of co-occurring patterns, out of hundreds to possibly thousands of irrelevant shots in videos being considered. To deal with this challenge, we developed a Maximal Biclique Finding (MBF) algorithm that is optimized to find sparsely co-occurring patterns, discarding less co-occurring patterns even if they are dominant in one video. Our algorithm is parallelizable with closed-form updates, thus can easily scale up to handle a large number of videos simultaneously. We demonstrate the effectiveness of our approach on motion capture and self-compiled YouTube datasets. Our results suggest that summaries generated by visual co-occurrence tend to match more closely with human generated summaries, when compared to several popular unsupervised techniques.
Watch and Learn: Semi-Supervised Learning for Object Detectors From Video
Authors: Ishan Misra, Abhinav Shrivastava, Martial Hebert
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.
Generalized Tensor Total Variation Minimization for Visual Data Recovery
Authors: Xiaojie Guo, Yi Ma
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In this paper, we propose a definition of Generalized Tensor Total Variation norm (GTV) that considers both the inhomogeneity and the multi-directionality of responses to derivative-like filters. More specifically, the inhomogeneity simultaneously preserves high-frequency signals and suppresses noises, while the multi-directionality ensures that, for an entry in a tensor, more information from its neighbors is taken into account. To effectively and efficiently seek the solution of the GTV minimization problem, we design a novel Augmented Lagrange Multiplier based algorithm, the convergence of which is theoretically guaranteed. Experiments are conducted to demonstrate the superior performance of our method over the state of the art alternatives on classic visual data recovery applications including completion and denoising.
Active Learning for Structured Probabilistic Models With Histogram Approximation
Authors: Qing Sun, Ankit Laddha, Dhruv Batra
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Abstract.  This paper studies active learning in structured probabilistic models such as Conditional Random Fields (CRFs). This is a challenging problem because unlike unstructured prediction problems such as binary or multi-class classification, structured prediction problems involve a distribution with an exponentially-large support, for instance, over the space of all possible segmentations of an image. Thus, the entropy of such models is typically intractable to compute. We propose a crude yet surprisingly effective histogram approximation to the Gibbs distribution, which replaces the exponentially-large support with a coarsened distribution that may be viewed as a histogram over M bins. We show that our approach outperforms a number of baselines and results in a 90%-reduction in the number of annotations needed to achieve nearly the same accuracy as learning from the entire dataset.
Bayesian Sparse Representation for Hyperspectral Image Super Resolution
Authors: Naveed Akhtar, Faisal Shafait, Ajmal Mian
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Despite the proven efficacy of hyperspectral imaging in many computer vision tasks, its widespread use is hindered by its low spatial resolution, resulting from hardware limitations. We propose a hyperspectral image super resolution approach that fuses a high resolution image with the low resolution hyperspectral image using non-parametric Bayesian sparse representation. The proposed approach first infers probability distributions for the material spectra in the scene and their proportions. The distributions are then used to compute sparse codes of the high resolution image. To that end, we propose a generic Bayesian sparse coding strategy to be used with Bayesian dictionaries learned with the Beta process. We theoretically analyze the proposed strategy for its accurate performance. The computed codes are used with the estimated scene spectra to construct the super resolution hyperspectral image. Exhaustive experiments on two public databases of ground based hyperspectral images and a remotely sensed image show that the proposed approach outperforms the existing state of the art.