mtybilly/Instruct2DS huggingface dataset:
Real-Time Coarse-to-Fine Topologically Preserving Segmentation
Authors: Marko Boben, Sanja Fidler, Raquel Urtasun Jian Yao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random field. We propose a coarse to fine optimization technique that speeds up inference in terms of the number of updates by an order of magnitude. Our approach is shown to outperform [31] while employing a single iteration. We evaluate and compare our approach to state-of-the-art superpixel algorithms on the BSD and KITTI benchmarks. Our approach significantly outperforms the baselines in the segmentation metrics and achieves the lowest error on the stereo task.
Supervised Mid-Level Features for Word Image Representation
Authors: Albert Gordo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper addresses the problem of learning word image representations: given the cropped image of a word, we are interested in finding a descriptive, robust, and compact fixed-length representation. Machine learning techniques can then be supplied with these representations to produce models useful for word retrieval or recognition tasks. Although many works have focused on the machine learning aspect once a global representation has been produced, little work has been devoted to the construction of those base image representations: most works use standard coding and aggregation techniques directly on top of standard computer vision features such as SIFT or HOG.  We propose to learn local mid-level features suitable for building word image representations. These features are learnt by leveraging character bounding box annotations on a small set of training images. However, contrary to other approaches that use character bounding box information, our approach does not rely on detecting the individual characters explicitly at testing time. Our local mid-level features can then be aggregated to produce a global word image signature. When pairing these features with the recent word attributes framework of Almaz\'an et al., we obtain results comparable with or better than the state-of-the-art on matching and recognition tasks using  global descriptors of only 96 dimensions.
Learning Lightness From Human Judgement on Relative Reflectance
Authors: Michael Maire, Stella X. Yu Takuya Narihira
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We develop a new approach to inferring lightness, the perceived reflectance of surfaces, from a single image.  Classic methods view this problem from the perspective of intrinsic image decomposition, where an image is separated into reflectance and shading components.  Rather than reason about reflectance and shading together, we learn to directly predict lightness differences between pixels.  Large-scale training from human judgement data on relative reflectance, and patch representations built using deep networks, provide the foundation for our model.  Benchmarked on the Intrinsic Images in the Wild dataset, our local lightness model achieves on-par performance with the state-of-the-art global lightness model, which incorporates multiple shading/reflectance priors and simultaneous reasoning between pairs of pixels in a dense conditional random field formulation.
Scene Classification With Semantic Fisher Vectors
Authors: Si Chen, Dashan Gao, Nikhil Rasiwasia, Nuno Vasconcelos Mandar Dixit
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: With the help of a convolutional neural network~(CNN) trained to recognize objects, a scene image is represented as a bag of semantics (BoS). This involves classifying image patches using the network and considering the class posterior probability vectors as locally extracted semantic descriptors. The image BoS is summarized using a Fisher vector~(FV) embedding that exploits the properties of the space of these descriptors. The resulting representation is referred to as a semantic Fisher vector. Two implementations of a semantic FV are investigated. First involves modeling the BoS with a Dirichlet Mixture and computing the Fisher gradients for this model. Due to the difficulty of mixture modeling on a non-Euclidean probability simplex, this approach is shown to be unsuccessful. A second implementation is derived using the interpretation of semantic descriptors as parameters of a multinomial distribution. Like the parameters of any exponential family, these can be projected into their natural parameter space. For a CNN, this is shown equivalent to using inputs of its soft-max layer as patch descriptors. A semantic FV is then computed as a Gaussian Mixture FV in the space of these natural parameters. This representation is shown to outperform other alternatives such as FVs of features from the intermediate CNN layers or a classifier obtained by adapting (fine-tuning) the CNN. The proposed FV represents an embedding for object classification probabilities. As an image representation, therefore, it is complementary to the features obtained from a scene classification CNN. A combination of the two representations is shown to achieve state-of-the-art results on MIT Indoor scenes and SUN datasets.
Don't Just Listen, Use Your Imagination: Leveraging Visual Common Sense for Non-Visual Tasks
Authors: Devi Parikh Xiao Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Artificial agents today can answer factual questions. But they fall short on questions that require common sense reasoning. Perhaps this is because most existing common sense databases rely on text to learn and represent knowledge. But much of common sense knowledge is unwritten - partly because it tends to not be interesting enough to talk about, and partly because some common sense is unnatural to articulate in text. While unwritten, it is not unseen. In this paper we leverage semantic common sense knowledge learned from images - i.e. visual common sense - in two textual tasks: fill-in-the-blank and visual paraphrasing. We propose to "imagine" the scene behind the text, and leverage visual cues from the "imagined" scenes in addition to textual cues while answering these questions. We imagine the scenes as a visual abstraction. Our approach outperforms a strong text-only baseline on these tasks. Our proposed tasks can serve as benchmarks to quantitatively evaluate progress in solving tasks that go "beyond recognition". Our code and datasets are publicly available.
Co-Saliency Detection via Looking Deep and Wide
Authors: Junwei Han, Chao Li, Jingdong Wang Dingwen Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: With the goal of effectively identifying common and salient objects in a group of relevant images, co-saliency detection has become essential for many applications such as video foreground extraction, surveillance, image retrieval, and image annotation. In this paper, we propose a unified co-saliency detection framework by introducing two novel insights: 1) looking deep to transfer higher-level representations by using the convolutional neural network with additional adaptive layers could better reflect the properties of the co-salient objects, especially their consistency among the image group; 2) looking wide to take advantage of the visually similar neighbors beyond a certain image group could effectively suppress the influence of the common background regions when formulating the intra-group consistency. In the proposed framework, the wide and deep information are explored for the object proposal windows extracted in each image, and the co-saliency scores are calculated by integrating the intra-image contrast and intra group consistency via a principled Bayesian formulation. Finally the window-level co-saliency scores are converted to the superpixel-level co-saliency maps through a foreground region agreement strategy. Comprehensive experiments on two benchmark datasets have demonstrated the consistent performance gain of the proposed approach.
Adopting an Unconstrained Ray Model in Light-Field Cameras for 3D Shape Reconstruction
Authors: Andrea Albarelli, Luca Cosmo, Andrea Torsello, Emanuele Rodola, Daniel Cremers Filippo Bergamasco
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Due to their recent availability as off-the-shelf commercial devices, light-field cameras has gathered increasing attention from both scientific community and industrial operators. However, their composite imaging formation process hinders the ability to exploit the well consolidated stack of calibration methods that are available for traditional cameras. While several efforts have been done to propose practical approaches, most of them still rely on the quasi-pinhole behaviour of the single microlens involved in the capturing process. This results in several drawbacks, ranging from the difficulties in feature detection, due to the reduced size of each microlens, to the need to adopt a model with a relatively small number of parameters. With this paper we propose to embrace a fully non-parametric model for the imaging and we show that it can be properly calibrated with little effort using a dense active target. This process produces a dense set of independent rays that cannot be directly used to produce a conventional image. However, they are an ideal tool for 3D reconstruction tasks, since they are highly redundant, very accurate and they cover a wide range of different baselines. The feasibility and convenience of the process and the accuracy of the obtained calibration are comprehensively evaluated through several experiments.
Towards 3D Object Detection With Bimodal Deep Boltzmann Machines Over RGBD Imagery
Authors: Rongrong Ji, Shaozi Li Wei Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Nowadays, detecting objects in 3D scenes like point clouds has become an emerging challenge with various applications. However, it retains as an open problem due to the deficiency of labeling 3D training data. To deploy an accurate detection algorithm typically resorts to investigating both RGB and depth modalities, which have distinct statistics while correlated with each other. Previous research mainly focus on detecting objects using only one modality, which ignores exploiting the cross-modality cues. In this work, we propose a cross-modality deep learning framework based on deep Boltzmann Machines for 3D Scenes object detection. In particular, we demonstrate that by learning cross-modality feature from RGBD data, it is possible to capture their joint information to reinforce detector trainings in individual modalities. In particular, we slide a 3D detection window in the 3D point cloud to match the exemplar shape, which the lack of training data in 3D domain is conquered via (1) We collect 3D CAD models and 2D positive samples from Internet. (2) adopt pretrained R-CNNs [2] to extract raw feature from both RGB and Depth domains. Experiments on RMRC dataset demonstrate that the bimodal based deep feature learning framework helps 3D scene object detection.
An Active Search Strategy for Efficient Object Class Detection
Authors: Alexander Vezhnevets, Vittorio Ferrari Abel Gonzalez-Garcia
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Object class detectors typically apply a window classifier to all the windows in a large set, either in a sliding window manner or using object proposals. In this paper, we develop an active search strategy that sequentially chooses the next window to evaluate based on all the information gathered before. This results in a substantial reduction in the number of classifier evaluations and in a more elegant approach in general. Our search strategy is guided by two forces. First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set. This enables to jump across distant regions in the image (e.g. observing a sky region suggests that cars might be far below) and is done efficiently in a Random Forest framework. Second, we exploit the score of the classifier to attract the search to promising areas surrounding a highly scored window, and to keep away from areas near low scored ones. Our search strategy can be applied on top of any classifier as it treats it as a black-box. In experiments with R-CNN on the challenging SUN2012 dataset, our method matches the detection accuracy of evaluating all windows independently, while evaluating 9x fewer windows.
Geodesic Exponential Kernels: When Curvature and Linearity Conflict
Authors: Francois Lauze, Soren Hauberg Aasa Feragen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We consider kernel methods on general geodesic metric spaces and provide both negative and positive results. First we show that the common Gaussian kernel can only be generalized to a positive definite kernel on a geodesic metric space if the space is flat. As a result, for data on a Riemannian manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic Laplacian kernel can be generalized while retaining positive definiteness. This implies that geodesic Laplacian kernels can be generalized to some curved spaces, including spheres and hyperbolic spaces. Our theoretical results are verified empirically.
Transformation-Invariant Convolutional Jungles
Authors: Joachim M. Buhmann Dmitry Laptev
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Many Computer Vision problems arise from information processing of data sources with nuisance variances like scale, orientation, contrast, perspective foreshortening or - in medical imaging - staining and local warping. In most cases these variances can be stated a priori and can be used to improve the generalization of recognition algorithms. We propose a novel supervised feature learning approach, which efficiently extracts information from these constraints to produce interpretable, transformation-invariant features. The proposed method can incorporate a large class of transformations, e.g., shifts, rotations, change of scale, morphological operations, non-linear distortions, photometric transformations, etc. These features boost the discrimination power of a novel image classification and segmentation method, which we call Transformation-Invariant Convolutional Jungles (TICJ). We test the algorithm on two benchmarks in face recognition and medical imaging, where it achieves state of the art results, while being computationally significantly more efficient than Deep Neural Networks.
Exemplar SVMs as Visual Feature Encoders
Authors: Patrick Perez Joaquin Zepeda
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In this work, we investigate the use of exemplar SVMs (linear SVMs trained with one positive example only and a vast collection of negative examples) as encoders that turn generic image features into new, task-tailored features. The proposed feature encoding leverages the ability of the exemplar-SVM (E-SVM) classifier to extract, from the original representation of the exemplar image, what is unique about it. While existing image description pipelines rely on the intuition of the designer to encode uniqueness into the feature encoding process, our proposed approach does it explicitly relative to a "universe" of features represented by the generic negatives. We show that such a post-processing enhances the performance of state-of-the art image retrieval methods based on aggregated image features, as well as the performance of nearest class mean and K-nearest neighbor image classification methods. We establish these advantages for several features, including "traditional" features as well as features derived from deep convolutional neural nets. As an additional contribution, we also propose a recursive extension of this E-SVM encoding scheme (RE-SVM) that provides further performance gains.
Object Scene Flow for Autonomous Vehicles
Authors: Andreas Geiger Moritz Menze
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which cannot be handled by existing methods.
Reflectance Hashing for Material Recognition
Authors: Kristin Dana, Ko Nishino Hang Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but  is challenging to measure and use for recognizing materials due to its high-dimensionality.  In this work, one-shot reflectance of a material surface which we refer to as a reflectance disk is capturing using a unique optical camera. The pixel coordinates of these reflectance disks correspond to the surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this  reflectance space  reveal the material class.  These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing.  We demonstrate  the effectiveness of reflectance hashing for material recognition with a number of real-world materials.
Joint Photo Stream and Blog Post Summarization and Exploration
Authors: Seungwhan Moon, Leonid Sigal Gunhee Kim
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose an approach that utilizes large collections of photo streams and blog posts, two of the most prevalent sources of data on the Web, for joint story-based summarization and exploration. Blogs consist of sequences of images and associated text; they portray events and experiences with concise sentences and representative images. We leverage blogs to help achieve story-based semantic summarization of collections of photo streams. In the opposite direction, blog posts can be enhanced with sets of photo streams by showing interpolations between consecutive images in the blogs. We formulate the problem of joint alignment from blogs to photo streams and photo stream summarization in a unified latent ranking SVM framework. We alternate between solving the two coupled latent SVM problems, by first fixing the summarization and solving for the alignment from blog images to photo streams and vice versa. On a newly collected large-scale Disneyland dataset of 10K blogs (120K associated images) and 6K photo streams (540K images), we demonstrate that blog posts and photo streams are mutually beneficial for summarization, exploration, semantic knowledge transfer, and photo interpolation.
Video Summarization by Learning Submodular Mixtures of Objectives
Authors: Helmut Grabner, Luc Van Gool Michael Gygli
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present a novel method for summarizing raw, casually captured videos. The objective is to create a short summary that still conveys the story. It should thus be both, interesting and representative for the input video. Previous methods often used simplified assumptions and only optimized for one of these goals. Alternatively, they used hand-defined objectives that were optimized sequentially by making consecutive hard decisions. This limits their use to a particular setting. Instead, we introduce a new method that  (i) uses a supervised approach in order to learn the importance of global characteristics of a summary and (ii) jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good summary. Experiments on two challenging and very diverse datasets demonstrate the effectiveness of our method, where we outperform or match current state-of-the-art.
Building Proteins in a Day: Efficient 3D Molecular Reconstruction
Authors: Ali Punjani, David J. Fleet Marcus A. Brubaker
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Discovering the 3D atomic structure of molecules such as proteins and viruses is a fundamental research problem in biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D structures from 2D images. This paper addresses the challenging problem of 3D reconstruction from 2D Cryo-EM images. A new framework for estimation is introduced which relies on modern stochastic optimization techniques to scale to large datasets. We also introduce a novel technique which reduces the cost of evaluating the objective function during optimization by over fiver orders of magnitude. The net result is an approach capable of estimating 3D molecular structure from large scale datasets in about a day on a single workstation.
Learning Descriptors for Object Recognition and 3D Pose Estimation
Authors: Vincent Lepetit Paul Wohlhart
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.
Image Partitioning Into Convex Polygons
Authors: Florent Lafarge Liuyun Duan
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our algorithm partitions images into convex polygons. It presents several interesting properties in terms of geometric guarantees, region compactness and scalability. The overall strategy consists in building a Voronoi diagram that conforms to preliminarily detected line-segments, before homogenizing the partition by spatial point process distributed over the image gradient. Our method is particularly adapted to images with strong geometric signatures, typically man-made objects and environments. We show the potential of our approach with experiments on large-scale images and comparisons with state-of-the-art superpixel methods.
Deep Visual-Semantic Alignments for Generating Image Descriptions
Authors: Li Fei-Fei Andrej Karpathy
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations.
Unsupervised Learning of Complex Articulated Kinematic Structures Combining Motion and Skeleton Information
Authors: Yiannis Demiris Hyung Jin Chang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively.
Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables
Authors: Pavan Turaga, Jingyong Su, Anuj Srivastava Rushil Anirudh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Human activities observed from visual sensors often give rise to a sequence of smoothly varying features. In many cases, the space of features can be formally defined as a manifold, where the action becomes a trajectory on the manifold. Such trajectories are high dimensional in addition to being non-linear, which can severely limit computations on them. We also argue that by their nature, human actions themselves lie on a much lower dimensional manifold compared to the high dimensional feature space. Learning an accurate low dimensional embedding for actions could have a huge impact in the areas of efficient search and retrieval, visualization, learning, and recognition. Traditional manifold learning addresses this problem for static points in R^n, but its extension to trajectories on Riemannian manifolds is non-trivial and has remained unexplored. The challenge arises due to the inherent non-linearity, and temporal variability that can significantly distort the distance metric between trajectories. To address these issues we use the transport square-root velocity function (TSRVF) space, a recently proposed representation that provides a metric which has favorable theoretical properties such as invariance to group action. We propose to learn the low dimensional embedding with a manifold functional variant of principal component analysis (mfPCA). We show that mfPCA effectively models the manifold trajectories in several applications such as action recognition, clustering and diverse sequence sampling while reducing the dimensionality by a factor of ~250x.  The mfPCA features can also be reconstructed back to the original manifold to allow for easy visualization of the latent variable space.
Show and Tell: A Neural Image Caption Generator
Authors: Alexander Toshev, Samy Bengio, Dumitru Erhan Oriol Vinyals
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.  The model is trained to maximize the likelihood of the target description sentence given the training image.  Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.
Descriptor Free Visual Indoor Localization With Line Segments
Authors: Horst Wildenauer Branislav Micusik
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present a novel view on the indoor visual localization problem, where we avoid the use of interest points and associated descriptors, which are the basic building blocks of most standard methods. Instead, localization is cast as an alignment problem of the edges of the query image to a 3D model consisting of line segments. The proposed strategy is effective in low-textured indoor environments and in very wide baseline setups as it overcomes the dependency of image descriptors on textures, as well as their limited invariance to view point changes. The basic features of our method, which are prevalent indoors, are line segments. As we will show, they allow for defining an efficient Chamfer distance-based aligning cost, computed through integral contour images, incorporated into a first-best-search strategy. Experiments confirm the  efectiveness of the method in terms of both, accuracy and computational complexity.
Fixation Bank: Learning to Reweight Fixation Candidates
Authors: Christian Siagian, Laurent Itti Jiaping Zhao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Predicting where humans will fixate in a scene has many practical applications. Biologically-inspired saliency models decompose visual stimuli into feature maps across multiple scales, and then integrate different feature channels, e.g., in a linear, MAX, or MAP. However, to date there is no universally accepted feature integration mechanism. Here, we propose a new a data-driven solution: We first build a "fixation bank" by mining training samples, which maintains the association between local patterns of activation, in 4 feature channels (color, intensity, orientation, motion) around a given location, and corresponding human fixation density at that location. During testing, we decompose feature maps into blobs, extract local activation patterns around each blob, match those patterns against the fixation bank by group lasso, and determine weights of blobs based on reconstruction errors. Our final saliency map is the weighted sum of all blobs. Our system thus incorporates some amount of spatial and featural context information into the location-dependent weighting mechanism. Tested on two standard data sets (DIEM for training and test, and CRCNS for test only; total of 23,670 training and 15,793 + 4,505 test frames), our model slightly but significantly outperforms 7 state-of-the-art saliency models.
Deep Networks for Saliency Detection via Local Estimation and Global Search
Authors: Huchuan Lu, Xiang Ruan, Ming-Hsuan Yang Lijun Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper presents a saliency detection algorithm by integrating both local estimation and global search. In the local estimation stage, we detect local saliency by using a deep neural network (DNN-L) which learns local patch features to determine the saliency value of each pixel. The estimated local saliency maps are further refined by exploring the high level object concept. In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions. Another deep neural network (DNN-G) is trained to predict the saliency score of each object region based on the global features. The final saliency map is generated by a weighted sum of salient object regions. Our method presents two interesting insights. First, local features learned by a supervised scheme can effectively capture local contrast, texture and shape information for saliency detection. Second, complex relationship between different global saliency cues can be captured by deep networks and exploited principally rather than heuristically. Quantitative and qualitative experiments on large benchmark data sets demonstrate that our algorithm performs favorably against the state-of-the-art methods.
Reflection Removal Using Ghosting Cues
Authors: Dilip Krishnan, Fredo Durand, William T. Freeman YiChang Shih
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Photographs taken through glass windows often contain both the desired scene and undesired reflections. Separating the reflection and transmission layers is an important but ill-posed problem that has both aesthetic and practical applications. In this work, we introduce the use of ghosting cues that introduce asymmetry between the layers, thereby helping to significantly reduce the ill-posedness of the problem. Such cues arise from shifted double reflections of the reflected scene off the glass surface. In double-pane windows, each pane reflects shifted and attenuated versions of objects on the same side of the glass as the camera. For single-pane windows, ghosting cues arise from shifted reflections on the two surfaces of the glass pane. Even though the ghosting is sometimes barely perceptible by humans, we can still exploit the cue for layer separation. In this work, we model the ghosted reflection using a double-impulse convolution kernel, and automatically estimate the spatial separation and relative attenuation of the ghosted reflection components. To separate the layers, we propose an algorithm that uses a Gaussian Mixture Model for regularization. Our method is automatic and requires only a single input image. We demonstrate that our approach removes a large fraction of reflections on both synthetic and real-world inputs.
A Dataset for Movie Description
Authors: Marcus Rohrbach, Niket Tandon, Bernt Schiele Anna Rohrbach
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length HD movies. In addition we also collected the aligned movie scripts which have been used in prior work and compare the two different sources of descriptions. In total the MPII Movie Description dataset (MPII-MD) contains a parallel corpus of over 68K sentences and video snippets from 94 HD movies. We characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are far more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production.
Fast and Robust Hand Tracking Using Detection-Guided Optimization
Authors: Franziska Mueller, Antti Oulasvirta, Christian Theobalt Srinath Sridhar
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Markerless tracking of hands and fingers is a promising enabler for human-computer interaction.  However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements. In this paper, we present a fast method for accurately tracking rapid and complex articulations of the hand using a single depth camera. Our algorithm uses a novel detection-guided optimization strategy that increases the robustness and speed of pose estimation. In the detection step, a randomized decision forest classifies pixels into parts of the hand. In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth. Our approach needs comparably less computational resources which makes it extremely fast (50 fps without GPU support). The approach also supports varying static, or moving, camera-to-scene arrangements. We show the benefits of our method by evaluating on public datasets and comparing against previous work.
Efficient SDP Inference for Fully-Connected CRFs Based on Low-Rank Decomposition
Authors: Chunhua Shen, Anton van den Hengel Peng Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Conditional Random Fields (CRFs) are one of the core technologies in computer vision, and have been applied on a wide variety of tasks. Conventional CRFs typically define edges between neighboring image pixels, resulting in a sparse graph over which inference can be performed efficiently. However, these CRFs fail to model more complex priors such as long-range contextual relationships. Fully-connected CRFs have thus been proposed. While there are efficient approximate inference methods for such CRFs, usually they are sensitive to initialization and make strong assumptions. In this work, we develop an efficient, yet general SDP algorithm for inference on fully-connected CRFs. The core of the proposed algorithm is a tailored quasi-Newton method, which solves a specialized SDP dual problem and takes advantage of the low-rank matrix approximation for fast computation. Experiments demonstrate that our method can be applied to fully-connected CRFs that could not previously be solved, such as those arising in pixel-level image co-segmentation.
Discriminative Learning of Iteration-Wise Priors for Blind Deconvolution
Authors: Dongwei Ren, Shuhang Gu, Liang Lin, Lei Zhang Wangmeng Zuo
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: The maximum a posterior (MAP)-based blind deconvolution framework generally involves two stages: blur kernel estimation and non-blind restoration. For blur kernel estimation, sharp edge prediction and carefully designed image priors are vital to the success of MAP. In this paper, we propose a blind deconvolution framework together with iteration specific priors for better blur kernel estimation. The family of hyper-Laplacian $( \Pr (\mathbf{d})\propto {{e}^{-{\left\| \mathbf{d} \right\|_{p}^{p}}/{\lambda }\;}})$ is adopted for modeling iteration-wise priors of image gradients, where each iteration has its own model parameters $\{\lambda^{(t)}, p^{(t)}\}$. To avoid heavy parameter tuning, all iteration-wise model parameters can be learned using our principled discriminative learning model from a training set, and can be directly applied to other dataset and real blurry images. Interestingly, with the generalized shrinkage / thresholding operator, negative $p$ value $(p < 0)$ is allowable and we find that it contributes more in estimating the coarse shape of blur kernel. Experimental results on synthetic and real world images demonstrate that our method achieves better deblurring results than the existing gradient prior-based methods. Compared with the state-of-the-art patch prior-based method, our method is competitive in restoration results but is much more efficient.
Eye Tracking Assisted Extraction of Attentionally Important Objects From Videos
Authors: Thuyen Ngo, Miguel Eckstein, B.S. Manjunath Karthikeyan Shanmuga Vadivel
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Visual attention is a crucial indicator of the relative importance of objects in visual scenes to human viewers. In this paper, we propose an algorithm to extract objects which attract visual attention from videos. As human attention is naturally biased towards high level semantic objects in visual scenes, this information can be valuable to extract salient objects. The proposed algorithm extracts dominant visual tracks using eye tracking data from multiple subjects on a video sequence by a combination of mean-shift clustering and Hungarian algorithm. These visual tracks guide a generic object search algorithm to get candidate object locations and extents in every frame. Further, we propose a novel multiple object extraction algorithm by constructing a spatio-temporal mixed graph over object candidates. Bounding box based object extraction inference is performed using binary linear integer programming on a cost function defined over the graph. Finally, the object boundaries are refined using grabcut segmentation. The proposed technique outperforms state-of-the-art video segmentation using eye tracking prior and obtains favorable object extraction over algorithms which do not utilize eye tracking data.
Multi-View Feature Engineering and Learning
Authors: Nikolaos Karianakis, Damek Davis, Joshua Hernandez, Jonathan Balzer, Stefano Soatto Jingming Dong
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We frame the problem of local representation of imaging data as the computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination. We show that, under very stringent conditions, these are related to "feature descriptors" commonly used in Computer Vision. Such conditions can be relaxed if multiple views of the same scene are available. We propose a sampling-based and a point-estimate based approximation of such a representation, compared empirically on image-to-(multiple)image matching, for which we introduce a multi-view wide-baseline matching benchmark, consisting of a mixture of real and synthetic objects with ground truth camera motion and dense three-dimensional geometry.
Self Scaled Regularized Robust Regression
Authors: Caglayan Dicle, Mario Sznaier, Octavia Camps Yin Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Linear Robust Regression (LRR) seeks to find the parameters of a linear mapping  from noisy data corrupted from outliers, such that the number of inliers (i.e. pairs of  points where the fitting error of the model  is less than a given bound) is maximized. While this problem is known to be NP hard, several tractable relaxations  have been recently proposed along with theoretical conditions guaranteeing exact recovery of the parameters of the model. However, these relaxations may perform poorly in cases  where the fitting error for the outliers is large. In addition, these approaches cannot exploit available a-priori information, such as co-occurrences. To circumvent these difficulties, in this paper we present an alternative approach to robust regression. Our main result shows that this approach is equivalent to a "self-scaled" l-1 regularized robust regression problem, where the cost function is automatically scaled, with scalings that depend on  the a-priori information. Thus, the proposed approach achieves substantially better performance than traditional  regularized approaches in cases where the outliers are far from the linear manifold spanned by the inliers, while at the same time exhibits the same theoretical recovery properties. These results are illustrated with several application examples using both synthetic and real data.
Simultaneous Feature Learning and Hash Coding With Deep Neural Networks
Authors: Yan Pan, Ye Liu, Shuicheng Yan Hanjiang Lai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) divide-and-encoding module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.