pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Group Objects
Authors: Victoria Yanulevskaya, Jasper Uijlings, Nicu Sebe
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Semantic Segmentation

This paper presents a novel method to generate a hypothesis set of class-independent object regions. It has been shown that such object regions can be used to focus computer vision techniques on the parts of an image that matter most leading to significant improvements in both object localisation and semantic segmentation in recent years. Of course, the higher quality of class-independent object regions, the better subsequent computer vision algorithms can perform. In this paper we focus on generating higher quality object hypotheses.  We start from an oversegmentation for which we propose to extract a wide variety of region-features. We group regions together in a hierarchical fashion, for which we train a Random Forest which predicts at each stage of the hierarchy the best possible merge. Hence unlike other approaches, we use relatively powerful features and classifiers at an early stage of the generation of likely object regions. Finally, we identify and combine stable regions in order to capture objects which consist of dissimilar parts. We show on the PASCAL 2007 and 2012 datasets that our method yields higher quality regions than competing approaches while it is at the same time more computationally efficient.
Unsupervised Multi-Class Joint Image Segmentation
Authors: Fan Wang, Qi-Xing Huang, Maks Ovsjanikov, Leonidas J. Guibas
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Joint segmentation of image sets is a challenging problem, especially when there are multiple objects with variable appearance shared among the images in the collection and the set of objects present in each particular image is itself varying and unknown. In this paper, we present a novel method to jointly segment a set of images containing objects from multiple classes.  We first establish consistent functional maps across the input images, and introduce a formulation that explicitly models partial similarity across images instead of global consistency. Given the optimized maps between pairs of images, multiple groups of consistent segmentation functions are found such that they align with segmentation cues in the images, agree with the functional maps, and are mutually exclusive. The proposed fully unsupervised approach exhibits a significant improvement over the state-of-the-art methods, as shown on the co-segmentation data sets MSRC, Flickr, and PASCAL.
Semantic Object Selection
Authors: Ejaz Ahmed, Scott Cohen, Brian Price
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Retrieval, Object, object-detection, Object Detection, Retrieval, Semantic Segmentation, TAG

Interactive object segmentation has great practical importance in computer vision. Many interactive methods have been proposed utilizing user input in the form of mouse clicks and mouse strokes, and often requiring a lot of user intervention. In this paper, we present a system with a far simpler input method: the user needs only give the name of the desired object.  With the tag provided by the user we do a text query of an image database to gather exemplars of the object. Using object proposals and borrowing ideas from image retrieval and object detection, the object is localized in the target image.  An appearance model generated from the exemplars and the location prior are used in an energy minimization framework to select the object. Our method outperforms the state-of-the-art on existing datasets and on a more challenging dataset we collected.
Discrete-Continuous Gradient Orientation Estimation for Faster Image Segmentation
Authors: Michael Donoser, Dieter Schmalstieg
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The state-of-the-art in image segmentation builds hierarchical segmentation structures based on analyzing local feature cues in spectral settings. Due to their impressive performance, such segmentation approaches have become building blocks in many computer vision applications. Nevertheless, the main bottlenecks are still the computationally demanding processes of local feature processing and spectral analysis. In this paper, we demonstrate that based on a discrete-continuous optimization of oriented gradient signals, we are able to provide segmentation performance competitive to state-of-the-art on BSDS 500 (even without any spectral analysis) while reducing computation time by a factor of 40 and memory demands by a factor of 10.
Object-based Multiple Foreground Video Co-segmentation
Authors: Huazhu Fu, Dong Xu, Bao Zhang, Stephen Lin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Segmentation

We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set. The use of object elements overcomes limitations of low-level feature representations in separating complex foregrounds and backgrounds. We formulate object-based co-segmentation as a co-selection graph in which regions with foreground-like characteristics are favored while also accounting for intra-video and inter-video foreground coherence. To handle multiple foreground objects, we expand the co-selection graph model into a proposed multi-state selection graph model (MSG) that optimizes the segmentations of different objects jointly. This extension into the MSG can be applied not only to our co-selection graph, but also can be used to turn any standard graph model into a multi-state selection solution that can be optimized directly by the existing energy minimization techniques. Our experiments show that our object-based multiple foreground video co-segmentation method (ObMiC) compares well to related techniques on both single and multiple foreground cases.
Parsing World's Skylines using Shape-Constrained MRFs
Authors: Rashmi Tonge, Subhransu Maji, C. V. Jawahar
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Segmentation

We propose an approach for segmenting the individual buildings in typical skyline images. Our approach is based on a Markov Random Field (MRF) formulation that exploits the fact that such images contain overlapping objects of similar shapes exhibiting a "tiered" structure. Our contributions are the following: (1) A dataset of 120 high-resolution skyline images from twelve different cities with over 4,000 individually labeled buildings that allows us to quantitatively evaluate the performance of various segmentation methods, (2) An analysis of low-level features that are useful for segmentation of buildings, and (3) A shape-constrained MRF formulation that enforces shape priors over the regions. For simple shapes such as rectangles, our formulation is significantly faster to optimize than a standard MRF approach, while also being more accurate. We experimentally evaluate various MRF formulations and demonstrate the effectiveness of our approach in segmenting skyline images.
Tell Me What You See and I will Show You Where It Is
Authors: Jia Xu, Alexander G. Schwing, Raquel Urtasun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Semantic Segmentation, Structured Prediction, Superpixels

We tackle the problem of weakly labeled semantic segmentation, where the only source of annotation are  image tags encoding  which classes are present in the  scene. This is an extremely difficult problem as no pixel-wise labelings are available, not even at training time. In this paper, we show that this problem can be formalized as an instance of  learning in a latent structured prediction framework, where the graphical model encodes the presence and absence of a class as well as the assignments of semantic labels to superpixels.  As a consequence,  we are able to leverage standard algorithms with good theoretical properties.  We demonstrate the effectiveness of our approach using the challenging SIFT-flow dataset  and show average per-class accuracy improvements  of   7%    over  the state-of-the-art.
Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision
Authors: Liang-Chieh Chen, Sanja Fidler, Alan L. Yuille, Raquel Urtasun
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Autonomous Driving

Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a  budget of tens or hundreds of thousands of dollars. Thus, developing solutions that can automatically perform the labeling given only weak supervision is key to reduce this cost. In this paper, we show how to exploit 3D information to automatically generate very accurate object segmentations given annotated 3D bounding boxes. We formulate the problem as the one of inference in a binary Markov random field which exploits appearance models, stereo and/or noisy point clouds, a repository of 3D CAD models as well as topological constraints. We demonstrate the effectiveness of our approach in the context of autonomous driving, and show that we can segment cars with the accuracy of 86% intersection-over-union, performing  as well as  highly recommended MTurkers!
Efficient Structured Parsing of Facades Using Dynamic Programming
Authors: Andrea Cohen, Alexander G. Schwing, Marc Pollefeys
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification

We propose a sequential optimization technique for segmenting a rectified image of a facade into semantic categories. Our method retrieves a parsing which respects common architectural constraints and also returns a certificate for global optimality. Contrasting the suggested method, the considered facade labeling problem is typically tackled as a classification task or as grammar parsing. Both approaches are not capable of fully exploiting the regularity of the problem.  Therefore, our technique very significantly improves the accuracy compared to the state-of-the-art while being an order of magnitude faster.  In addition, in 85% of the test images we obtain a certificate for optimality.
Dense Semantic Image Segmentation with Objects and Attributes
Authors: Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother, Philip H. S. Torr
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Attribute, Image Segmentation, Object, Semantic Segmentation

The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. "I see a shiny red chair'). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.
Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors
Authors: Felix Heide, Lei Xiao, Wolfgang Heidrich, Matthias B. Hullin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Reconstruction

The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.
Fourier Analysis on Transient Imaging with a Multifrequency Time-of-Flight Camera
Authors: Jingyu Lin, Yebin Liu, Matthias B. Hullin, Qionghai Dai
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

A transient image is the optical impulse response of a scene which visualizes light propagation during an ultra-short time interval. In this paper we discover that the data captured by a multifrequency time-of-flight (ToF) camera is the Fourier transform of a transient image, and identify the sources of systematic error. Based on the discovery we propose a novel framework of frequency-domain transient imaging, as well as algorithms to remove systematic error. The whole process of our approach is of much lower computational cost, especially lower memory usage, than Heide et al.'s approach using the same device. We evaluate our approach on both synthetic and real-datasets.
Transparent Object Reconstruction via Coded Transport of Intensity
Authors: Chenguang Ma, Xing Lin, Jinli Suo, Qionghai Dai, Gordon Wetzstein
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Object, Object Reconstruction

Capturing and understanding visual signals is one of the core interests of computer vision. Much progress has been made w.r.t. many aspects of imaging, but the reconstruction of refractive phenomena, such as turbulence, gas and heat flows, liquids, or transparent solids, has remained a challenging problem. In this paper, we derive an intuitive formulation of light transport in refractive media using light fields and the transport of intensity equation. We show how coded illumination in combination with pairs of recorded images allow for robust computational reconstruction of dynamic two and three-dimensional refractive phenomena.
3D Shape and Indirect Appearance by Structured Light Transport
Authors: Matthew O'Toole, John Mather, Kiriakos N. Kutulakos
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

We consider the problem of deliberately manipulating the direct and indirect light flowing through a time-varying, fully-general scene in order to simplify its visual analysis. Our approach rests on a crucial link between stereo geometry and light transport: while direct light always obeys the epipolar geometry of a projector-camera pair, indirect light overwhelmingly does not. We show that it is possible to turn this observation into an imaging method that analyzes light transport in real time in the optical domain, prior to acquisition. This yields three key abilities that we demonstrate in an experimental camera prototype: (1) producing a live indirect-only video stream for any scene, regardless of geometric or photometric complexity; (2) capturing images that make existing structured-light shape  recovery algorithms robust to indirect transport; and (3) turning them into one-shot methods for dynamic 3D shape capture.
Shape-Preserving Half-Projective Warps for Image Stitching
Authors: Che-Han Chang, Yoichi Sato, Yung-Yu Chuang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Stitching

This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation. Given the projective transformation relating two input images, based on an analysis of the projective transformation, our method smoothly extrapolates the projective transformation of the overlapping regions into the non-overlapping regions and the resultant warp gradually changes from projective to similarity across the image. The proposed warp has the strengths of both projective and similarity warps. It provides good alignment accuracy as projective warps while preserving the perspective of individual image as similarity warps. It can also be combined with more advanced local-warp-based alignment methods such as the as-projective-as-possible warp for better alignment accuracy. With the proposed warp, the field of view can be extended by stitching images with less projective distortion (stretched shapes and enlarged sizes).
Parallax-tolerant Image Stitching
Authors: Fan Zhang, Feng Liu
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Image Stitching, Local Distortion

Parallax handling is a challenging task for image stitching. This paper presents a local stitching method to handle parallax based on the observation that input images do not need to be perfectly aligned over the whole overlapping region for stitching. Instead, they only need to be aligned in a way that there exists a local region where they can be seamlessly blended together. We adopt a hybrid alignment model that combines homography and content-preserving warping to provide flexibility for handling parallax and avoiding objectionable local distortion. We then develop an efficient randomized algorithm to search for a homography, which, combined with content-preserving warping, allows for optimal stitching. We predict how well a homography enables plausible stitching by finding a plausible seam and using the seam cost as the quality metric. We develop a seam finding method that estimates a plausible seam from only roughly aligned images by considering both geometric alignment and image content. We then pre-align input images using the optimal homography and further use content-preserving warping to locally refine the alignment. We finally compose aligned images together using a standard seam-cutting algorithm and a multi-band blending algorithm. Our experiments show that our method can effectively stitch images with large parallax that are difficult for existing methods.
Learning Everything about Anything: Webly-Supervised Visual Concept Learning
Authors: Santosh K. Divvala, Ali Farhadi, Carlos Guestrin
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6

Recognition is graduating from labs to real-world applications. While it is encouraging to see its potential being tapped, it brings forth a fundamental challenge to the vision researcher: scalability. How can we learn a model for any concept that exhaustively covers all its appearance variations, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models? In this paper, we introduce a fully-automated approach for learning extensive models for a wide range of variations (e.g. actions, interactions, attributes and beyond) within any concept. Our approach leverages vast resources of online books to discover the vocabulary of variance, and intertwines the data collection and modeling steps to alleviate the need for explicit human supervision in training the models. Our approach organizes the visual knowledge about a concept in a convenient and useful way, enabling a variety of applications across vision and NLP. Our online system has been queried by users to learn models for several interesting concepts including breakfast, Gandhi, beautiful, etc. To date, our system has models available for over 50,000 variations within 150 concepts, and has annotated more than 10 million images with bounding boxes.
Dirichlet-based Histogram Feature Transform for Image Classification
Authors: Takumi Kobayashi
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: Classification, General Classification, image-classification, Image Classification

Histogram-based features have significantly contributed to recent development of image classifications, such as by SIFT local descriptors. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. The (L1-normalized) histogram feature is regarded as a probability mass function, which is modeled by Dirichlet distribution. Based on the probabilistic modeling, we induce the Dirichlet Fisher kernel for transforming the histogram feature vector. The method works on the individual histogram feature to enhance the discriminative power at a low computational cost. On the other hand, in the bag-of-feature (BoF) framework, the Dirichlet mixture model can be extended to Gaussian mixture by transforming histogram-based local descriptors, e.g., SIFT, and thereby we propose the method of Dirichlet-derived GMM Fisher kernel. In the experiments on diverse image classification tasks including recognition of subordinate objects and material textures, the proposed methods improve the performance of the histogram-based features and BoF-based Fisher kernel, being favorably competitive with the state-of-the-arts.
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Authors: Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, Philip Torr
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: CPU, Object, object-detection, Object Detection

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure.  We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single laptop CPU) generates a small set of category-independent, high quality object windows, yielding 96.2% object detection rate (DR) with 1,000 proposals. Increasing the numbers of proposals and color spaces for computing BING features, our performance can be further improved to 99.5% DR.
Context Driven Scene Parsing with Attention to Rare Classes
Authors: Jimei Yang, Brian Price, Scott Cohen, Ming-Hsuan Yang
Date: 2014-06-01 00:00:00
Proceeding: CVPR 2014 6
Tasks: General Classification, Image Retrieval, Retrieval, Scene Parsing

This paper presents a scalable scene parsing algorithm based on image retrieval and superpixel matching. We focus on rare object classes, which play an important role in achieving richer semantic understanding of visual scenes, compared to common background classes. Towards this end, we make two novel contributions: rare class expansion and semantic context description. First, considering the long-tailed nature of the label distribution, we expand the retrieval set by rare class exemplars and thus achieve more balanced superpixel classification results. Second, we incorporate both global and local semantic context information through a feedback based mechanism to refine image retrieval and superpixel matching. Results on the SIFTflow and LMSun datasets show the superior performance of our algorithm, especially on the rare classes, without sacrificing overall labeling accuracy.