pwc-archive/papers-with-abstracts huggingface dataset:
Inverting RANSAC: Global Model Detection via Inlier Rate Estimation
Authors: Roee Litman, Simon Korman, Alexander Bronstein, Shai Avidan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Homography Estimation, Translation

This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optimal transformation. We provide a theoretical analysis of the IRE method using a stochastic generative model on the continuous spaces of matches and transformations. This model allows rigorous investigation of the limits of our IRE method for the case of 2D-translation, further giving bounds and insights for the more general case. Our theoretical analysis is validated empirically and is shown to hold in practice for the more general case of 2D-affinities. In addition, we show that the combined framework works on challenging cases of 2D-homography estimation, with very few and possibly noisy inliers, where RANSAC generally fails.
Robust Multi-Image Based Blind Face Hallucination
Authors: Yonggang Jin, Christos-Savvas Bouganis
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Face Hallucination, Hallucination, Super-Resolution

This paper proposes a robust multi-image based blind face hallucination framework to super-resolve LR faces. The proposed framework first estimates both blurring kernel and transformations of multiple LR faces by robust deblurring and registration in PCA subspace. A patch-wise mixture of probabilistic PCA prior is then incorporated for face super-resolution. Previous work on face SR using PCA prior can be viewed as special cases of the framework. Experimental results in both simulated and real LR sequences demonstrate very promising performance of the proposed method.
A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition
Authors: Dihong Gong, Zhifeng Li, DaCheng Tao, Jianzhuang Liu, Xuelong. Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Age-Invariant Face Recognition, Face Recognition, MORPH

In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition. First, a new maximum entropy feature descriptor (MEFD) is developed that encodes the microstructure of facial images into a set of discrete codes in terms of maximum entropy. By densely sampling the encoded face image, sufficient discriminatory and expressive information can be extracted for further analysis. A new matching method is also developed, called identity factor analysis (IFA), to estimate the probability that two faces have the same underlying identity. The effectiveness of the framework is confirmed by extensive experimentation on two face aging datasets, MORPH (the largest public-domain face aging dataset) and FGNET. We also conduct experiments on the famous LFW dataset to demonstrate the excellent generalizability of our new approach.
Sense Discovery via Co-Clustering on Images and Text
Authors: Xinlei Chen, Alan Ritter, Abhinav Gupta, Tom Mitchell
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering

We present a co-clustering framework that can be used to discover multiple semantic and visual senses of a given Noun Phrase (NP). Unlike traditional clustering approaches which assume a one-to-one mapping between the clusters in the text-based feature space and the visual space, we adopt a one-to-many mapping between the two spaces. This is primarily because each semantic sense (concept) can correspond to different visual senses due to viewpoint and appearance variations. Our structure-EM style optimization not only extracts the multiple senses in both semantic and visual feature space, but also discovers the mapping between the senses. We introduce a challenging dataset (CMU Polysemy-30) for this problem consisting of 30 NPs ($\sim$5600 labeled instances out of $\sim$22K total instances). We have also conducted a large-scale experiment that performs sense disambiguation for $\sim$2000 NPs.
An Approximate Shading Model for Object Relighting
Authors: Zicheng Liao, Kevin Karsch, David Forsyth
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: model, Object

We propose an approximate shading model for image-based object modeling and insertion. Our approach is a hybrid of 3D rendering and image-based composition. It avoids the difficulties of physically accurate shape estimation from a single image, and allows for more flexible image composition than pure image-based methods. The model decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties.  With this object model, we build an object relighting system that allows an artist to select an object from an image and insert it into a 3D scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears more naturally in the scene. Our quantitative evaluation and extensive user study suggest our method is a promising alternative to existing methods of object insertion.
Deep Domain Adaptation for Describing People Based on Fine-Grained Clothing Attributes
Authors: Qiang Chen, Junshi Huang, Rogerio Feris, Lisa M. Brown, Jian Dong, Shuicheng Yan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Attribute, Domain Adaptation

We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose/lighting/background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed in-between the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes.
A Convolutional Neural Network Cascade for Face Detection
Authors: Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, Gang Hua
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: CPU, Face Detection, GPU

In real-world face detection, large visual variations, such as those due to pose, expression, and lighting, demand an advanced discriminative model to accurately differentiate faces from the backgrounds.  Consequently, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose a cascade architecture built on convolutional neural networks (CNNs) with very powerful discriminative capability, while maintaining high performance. The proposed CNN cascade operates at multiple resolutions, quickly rejects the background regions in the fast low resolution stages, and carefully evaluates a small number of challenging candidates in the last high resolution stage. To improve localization effectiveness, and reduce the number of candidates at later stages, we introduce a CNN-based calibration stage after each of the detection stages in the cascade.  The output of each calibration stage is used to adjust the detection window position for input to the subsequent stage.  The proposed method runs at 14 FPS on a single CPU core for VGA-resolution images and 100 FPS using a GPU, and achieves state-of-the-art detection performance on two public face detection benchmarks.
Visual Vibrometry: Estimating Material Properties From Small Motion in Video
Authors: Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Fredo Durand, William T. Freeman
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Scene Understanding

The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motion in video. Objects tend to vibrate in a set of preferred modes. The shapes and frequencies of these modes depend on the structure and material properties of an object. Focusing on the case where geometry is known or fixed, we show how information about an object's modes of vibration can be extracted from video and used to make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of rods and fabrics by passively observing their motion in high-speed and regular framerate video.
Fine-Grained Histopathological Image Analysis via Robust Segmentation and Large-Scale Retrieval
Authors: Xiaofan Zhang, Hai Su, Lin Yang, Shaoting Zhang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval

Computer-aided diagnosis of medical images requires thorough analysis of image details. For example, examining all cells enables fine-grained categorization of histopathological images. Traditional computational methods may have efficiency issues when performing such detailed analysis. In this paper, we propose a robust and scalable solution to achieve this. Specifically, a robust segmentation method is developed to delineate region-of-interests (e.g., cells) accurately, using hierarchical voting and repulsive active contour. A hashing-based large-scale retrieval approach is also designed to examine and classify them by comparing with a massive training database. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and the squamous carcinoma), using thousands of histopathological images extracted from hundreds of patients. Our method has achieved promising performance, i.e., 87.3% accuracy and 1.68 seconds by searching among half-million cells.
L0TV: A New Method for Image Restoration in the Presence of Impulse Noise
Authors: Ganzhao Yuan, Bernard Ghanem
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Denoising, Image Denoising, Image Restoration

Total Variation (TV) is an effective and popular prior model in the field of regularization-  image processing. This paper focuses on TV for image restoration  in the presence of impulse noise. This type of noise frequently arises in data acquisition and  transmission due to many reasons, e.g. a faulty sensor or analog-to-digital converter errors. Removing this noise  is an important task in image restoration. State-of-the-art methods such as Adaptive Outlier Pursuit(AOP) [42], which is based on TV with L02-norm data fidelity, only give sub-optimal performance. In this paper, we propose a new method, called L0TV-PADMM, which solves the TV-based restoration problem with L0-norm data fidelity. To effectively deal with the resulting non-convex non-smooth optimization problem, we first reformulate it as an equivalent MPEC (Mathematical Program with Equilibrium Constraints), and then solve it using a proximal Alternating Direction Method of Multipliers (PADMM). Our L0TV-PADMM method finds a desirable solution to the original L0-norm optimization problem and is proven to be convergent under mild conditions. We apply L0TV-PADMM to the problems of image denoising and deblurring in the presence of impulse noise. Our extensive experiments demonstrate that L0TV-PADMM outperforms state-of-the-art image restoration methods.
Modeling Video Evolution for Action Recognition
Authors: Basura Fernando, Efstratios Gavves, Jose Oramas M., Amir Ghodrati, Tinne Tuytelaars
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

In this paper we present a method to capture video-wide temporal information for action recognition. We postulate that a function capable of ordering the frames of a video temporally (based on the appearance) captures well the evolution of the appearance within the video. We learn such ranking functions per video via a ranking machine and use the parameters of these as a new video representation. The proposed method is easy to interpret and implement, fast to compute and effective in recognizing a wide variety of actions.  We perform a large number of evaluations on datasets for generic action recognition (Hollywood2 and HMDB51), fine-grained actions (MPII- cooking activities) and gestures (Chalearn). Results show that the proposed method  brings an absolute improvement of 7-10\%, while being compatible with and complementary to further improvements in appearance and local motion based methods.
Long-Term Correlation Tracking
Authors: Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Translation, Visual Tracking

In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-the-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn the discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness.
Joint Tracking and Segmentation of Multiple Targets
Authors: Anton Milan, Laura Leal-Taixe, Konrad Schindler, Ian Reid
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Video Segmentation, Video Semantic Segmentation

Tracking-by-detection has proven to be the most successful strategy to address the task of tracking multiple targets in unconstrained scenarios. Traditionally, a set of sparse detections, generated in a preprocessing step, serves as input to a high-level tracker whose goal is to correctly associate these "dots" over time. An obvious shortcoming of this approach is that most information available in image sequences is simply ignored by thresholding weak detection responses and applying non-maximum suppression. We propose a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background. As a result, we obtain an video segmentation in addition to the classical bounding-box representation in unconstrained, real-world sequences. Our method shows encouraging results on many standard benchmark sequences and significantly outperforms state-of-the-art tracking-by-detection approaches in crowded scenes with long-term partial occlusions.
RGBD-Fusion: Real-Time High Precision Depth Recovery
Authors: Roy Or - El, Guy Rosman, Aaron Wetzler, Ron Kimmel, Alfred M. Bruckstein
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Vocal Bursts Intensity Prediction

The popularity of low-cost RGB-D scanners is increasing on a daily basis. Nevertheless, existing scanners often cannot capture subtle details in the environment. We present a novel method to enhance the depth map by fusing the intensity and depth information to create more detailed range profiles. The lighting model we use can handle natural scene illumination. It is integrated in a shape from shading like technique to improve the visual fidelity of the reconstructed object. Unlike previous efforts in this domain, the detailed geometry is calculated directly, without the need to explicitly find and integrate surface normals. In addition, the proposed method operates four orders of magnitude faster than the state of the art. Qualitative and quantitative visual and statistical evidence support the improvement in the depth obtained by the suggested method.
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution
Authors: Yu Zhu, Yanning Zhang, Boyan Bonev, Alan L. Yuille
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Super-Resolution, Super-Resolution, Triplet

We propose a single-image super-resolution method based on the gradient reconstruction. To predict the gradient field, we collect a dictionary of gradient patterns from an external set of images. We observe that there are patches representing singular primitive structures (e.g. a single edge), and non-singular ones (e.g. a triplet of edges). Based on the fact that singular primitive patches are more invariant to the scale change (i.e. have less ambiguity across different scales), we represent the non-singular primitives as compositions of singular ones, each of which is allowed some deformation. Both the input patches and dictionary elements are decomposed to contain only singular primitives. The compositional aspect of the model makes the gradient field more reliable. The deformable aspect makes the dictionary more expressive. As shown in our experimental results, the proposed method outperforms the state-of-the-art methods.
Active Pictorial Structures
Authors: Epameinondas Antonakos, Joan Alabort-i-Medina, Stefanos Zafeiriou
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Alignment

In this paper we present a novel generative deformable model motivated by Pictorial Structures (PS) and Active Appearance Models (AAMs) for object alignment in-the-wild. Inspired by the tree structure used in PS, the proposed Active Pictorial Structures (APS) model the appearance of the object using multiple graph-based pairwise normal distributions (Gaussian Markov Random Field) between the patches extracted from the regions around adjacent landmarks. We show that this formulation is more accurate than using a single multivariate distribution (Principal Component Analysis) as commonly done in the literature. APS employ a weighted inverse compositional Gauss-Newton optimization with fixed Jacobian and Hessian that achieves close to real-time performance and state-of-the-art results. Finally, APS have a spring-like graph-based deformation prior term that makes them robust to bad initializations. We present extensive experiments on the task of face alignment, showing that APS outperform current state-of-the-art methods. To the best of our knowledge, the proposed method is the first weighted inverse compositional technique that proves to be so accurate and efficient at the same time.
Ego-Surfing First-Person Videos
Authors: Ryo Yonetani, Kris M. Kitani, Yoichi Sato
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We envision a future time when wearable cameras (e.g., small cameras in glasses or pinned on a shirt collar) are worn by the masses and record first-person point-of-view (POV) videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera -- with or without consent. Motivated by these benefits and risks, we develop a self-search technique tailored to first-person POV videos. The key observation of our work is that the egocentric head motions of a target person (i.e., the self) are observed both in the POV video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to uniquely identify instances of the self. We incorporate this feature into our proposed approach that computes the motion correlation over supervoxel hierarchies to localize target instances in observer videos. Our proposed approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, automated video collection and social group discovery.
Recovering Inner Slices of Translucent Objects by Multi-Frequency Illumination
Authors: Kenichiro Tanaka, Yasuhiro Mukaigawa, Hiroyuki Kubo, Yasuyuki Matsushita, Yasushi Yagi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper describes a method for recovering appearance of inner slices of translucent objects. The outer appearance of translucent objects is a summation of the appearance of slices at all depths, where each slice is blurred by depth-dependent point spread functions (PSFs). By exploiting the difference of low-pass characteristics of depth-dependent PSFs, we develop a multi-frequency illumination method for obtaining the appearance of individual inner slices using a coaxial projector-camera setup. Specifically, by measuring the target object with varying the spatial frequency of checker patterns emitted from a projector, our method recovers inner slices via a simple linear solution method. We quantitatively evaluate accuracy of the proposed method by simulations and show qualitative recovery results using real-world scenes.
How Many Bits Does it Take for a Stimulus to Be Salient?
Authors: Sayed Hossein Khatoonabadi, Nuno Vasconcelos, Ivan V. Bajic, Yufeng Shan
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Saliency Prediction

Visual saliency has been shown to depend on the unpredictability of the visual stimulus given its surround. Various previous works have advocated the equivalence between stimulus saliency and uncompressibility. We propose a direct measure of this quantity, namely the number of bits required by an optimal video compressor to encode a given video patch, and show that features derived from this measure are highly predictive of eye fixations. To account for global saliency effects, these are embedded in a Markov random field model. The resulting saliency measure is shown to achieve state-of-the-art accuracy for the prediction of fixations, at a very low computational cost. Since most modern cameras incorporate video encoders, this paves the way for in-camera saliency estimation, which could be useful in a variety of computer vision applications.