pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Propose Objects
Authors: Philipp Krahenbuhl, Vladlen Koltun
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Combinatorial Optimization, Segmentation, Semantic Segmentation

We present an approach for highly accurate bottom-up object segmentation. Given an image, the approach rapidly generates a set of regions that delineate candidate objects in the image. The key idea is to train an ensemble of figure-ground segmentation models. The ensemble is trained jointly, enabling individual models to specialize and complement each other. We reduce ensemble training to a sequence of uncapacitated facility location problems and show that highly accurate segmentation ensembles can be trained by combinatorial optimization. The training procedure jointly optimizes the size of the ensemble, its composition, and the parameters of incorporated models, all for the same objective. The ensembles operate on elementary image features, enabling rapid image analysis. Extensive experiments demonstrate that the presented approach outperforms prior object proposal algorithms by a significant margin, while having the lowest running time. The trained ensembles generalize across datasets, indicating that the presented approach is capable of learning a generally applicable model of bottom-up segmentation.
Basis Mapping Based Boosting for Object Detection
Authors: Haoyu Ren, Ze-Nian Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection, Pedestrian Detection

We propose a novel mapping method to improve the training accuracy and  efficiency of boosted classifiers for object detection. The key step of the proposed method is a non linear mapping on original samples by referring to the basis samples before feeding into the weak classifiers, where the basis samples correspond to the hard samples in the current training stage. We show that the basis mapping based weak classifier is an approximation of kernel weak classifiers while keeping the same computation cost as linear weak classifiers. As a result, boosting with such weak classifiers is more effective. In this paper, two different non-linear mappings are shown to work well. We adopt the LogitBoost algorithm to train the weak classifiers based on the  Histogram of Oriented Gradient descriptor (HOG).  Experimental results show that the proposed approach significantly improves the detection accuracy and training efficiency of the boosted classifier. It also achieves performance comparable with the commonly used methods on public datasets for both pedestrian detection and general object detection tasks.
Recognize Complex Events From Static Images by Fusing Deep Channels
Authors: Yuanjun Xiong, Kai Zhu, Dahua Lin, Xiaoou Tang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

A considerable portion of web images capture events that occur in our personal lives or social activities. In this paper, we aim to develop an effective method for recognizing events from such images. Despite the sheer amount of study on event recognition, most existing methods rely on videos and are not directly applicable to this task. Generally, events are complex phenomena that involve interactions among people and objects, and therefore analysis of event photos requires techniques that can go beyond recognizing individual objects and carry out joint reasoning based on evidences of multiple aspects. Inspired by the recent success of deep learning, we formulate a multi-layer framework to tackle this problem, which takes into account both visual appearance and the interactions among humans and objects, and combines them via semantic fusion. An important issue arising here is that humans and objects discovered by detectors are in the form of bounding boxes, and there is no straightforward way to represent their interactions and incorporate them with a deep network. We address this using a novel strategy that projects the detected instances onto multi-scale spatial maps. On a large dataset with $60,000$ images, the proposed method achieved substantial improvement over the state-of-the-art, raising the accuracy of event recognition by over $10\%$.
Multi-Feature Max-Margin Hierarchical Bayesian Model for Action Recognition
Authors: Shuang Yang, Chunfeng Yuan, Baoxin Wu, Weiming Hu, Fangshi Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, General Classification, Multi-Task Learning, Temporal Action Localization

In this paper, a multi-feature max-margin hierarchical Bayesian model (M3HBM) is proposed for action recognition. Different from existing methods which separate representation and classification into two steps, M3HBM jointly learns a high-level representation by combining a hierarchical generative model (HGM) and discriminative max-margin classifiers in a unified Bayesian framework. Specifically, HGM is proposed to represent actions by distributions over latent spatial temporal patterns (STPs) which are learned from multiple feature modalities and shared among different classes. For recognition, we employ Gibbs classifiers to minimize the expected loss function based on the max-margin principle and use the classifiers as regularization terms of M3HBM to perform Bayeisan estimation for classifier parameters together with the learning of STPs. In addition, multi-task learning is applied to learn the model from multiple feature modalities for different classes. For test videos, we obtain the representations by the inference process and perform action recognition by the learned Gibbs classifiers. For the learning and inference process, we derive an efficient Gibbs sampling algorithm to solve the proposed M3HBM. Extensive experiments on several datasets demonstrate both the representation power and the classification capability of our approach for action recognition.
Model Recommendation: Generating Object Detectors From Few Samples
Authors: Yu-Xiong Wang, Martial Hebert
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object

In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples. Instead, we assume that we have evaluated ''off-line'' a large library of detectors against a large set of detection tasks. Given a new target task, we evaluate a subset of the models on few samples from the new task and we use the matrix of models-tasks ratings to predict the performance of all the models in the library on the new task, enabling us to select a good set of detectors for the new task. This approach has three key advantages of great interest in practice: 1) generating a large collection of expressive models in an unsupervised manner is possible; 2) a far smaller set of annotated samples is needed compared to that required for training from scratch; and 3) recommending models is a very fast operation compared to the notoriously expensive training procedures of modern detectors. (1) will make the models informative across different categories; (2) will dramatically reduce the need for manually annotating vast datasets for training detectors; and (3) will enable rapid generation of new detectors.
A Linear Least-Squares Solution to Elastic Shape-From-Template
Authors: Abed Malti, Adrien Bartoli, Richard Hartley
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Position

We cast SfT (Shape-from-Template) as the search of a vector field (X,dX), composed of the pose X and the displacement  dX that produces the deformation. We propose the first fully linear least-squares SfT method modeling elastic deformations. It relies on a set of Solid Boundary Constraints SBC to position the template at X in the deformed frame. The displacement is mapped by the stiffness matrix to minimize the  amount of force responsible for the deformation. This linear minimization is subjected to the Reprojection Boundary Constraints RBC of the deformed shape X+dX on the deformed image. Compared to state-of-the-art methods, this new formulation allows us to obtain accurate results at a low computational cost.
Robust Large Scale Monocular Visual SLAM
Authors: Guillaume Bourmaud, Remi Megret
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Simultaneous Localization and Mapping

This paper deals with the trajectory estimation of a monocular calibrated camera evolving in a large unknown environment, also known as monocular visual simultaneous localization and mapping. The contribution of this paper is threefold: 1) We develop a new formalism that builds upon the so called Known Rotation Problem to robustly estimate submaps (parts of the camera trajectory and the unknown environment). 2) In order to obtain a globally consistent map (up to a scale factor), we propose a novel loopy belief propagation algorithm that is able to efficiently align a large number of submaps. Our approach builds a graph of relative 3D similarities (computed between the submaps) and estimates the global 3D similarities by passing messages through a super graph until convergence. 3) To render the whole framework more robust, we also propose a simple and efficient outlier removal algorithm that detects outliers in the graph of relative 3D similarities. We extensively demonstrate, on the TUM and KITTI benchmarks as well as on several other challenging video sequences, that the proposed method outperforms the state of the art algorithms.
Membership Representation for Detecting Block-Diagonal Structure in Low-Rank or Sparse Subspace Clustering
Authors: Minsik Lee, Jieun Lee, Hyeogjin Lee, Nojun Kwak
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Philosophy

Recently, there have been many proposals with state-of-the-art results in subspace clustering that take advantages of the low-rank or sparse optimization techniques. These methods are based on self-expressive models, which have well-defined theoretical aspects. They produce matrices with (approximately) block-diagonal structure, which is then applied to spectral clustering. However, there is no definitive way to construct affinity matrices from these block-diagonal matrices and it is ambiguous how the performance will be affected by the construction method. In this paper, we propose an alternative approach to detect block-diagonal structures from these matrices. The proposed method shares the philosophy of the above subspace clustering methods, in that it is a self-expressive system based on a Hadamard product of a membership matrix. To resolve the difficulty in handling the membership matrix, we solve the convex relaxation of the problem and then transform the representation to a doubly stochastic matrix, which is closely related to spectral clustering. The result of our method has eigenvalues normalized in between zero and one, which is more reliable to estimate the number of clusters and to perform spectral clustering. The proposed method shows competitive results in our experiments, even though we simply count the number of eigenvalues larger than a certain threshold to find the number of clusters.
Bayesian Inference for Neighborhood Filters With Application in Denoising
Authors: Chao-Tsung Huang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Bayesian Inference, Color Image Denoising, Denoising, Image Denoising, parameter estimation

Range-weighted neighborhood filters are useful and popular for their edge-preserving property and simplicity, but they are originally proposed as intuitive tools. Previous works needed to connect them to other tools or models for indirect property reasoning or parameter estimation. In this paper, we introduce a unified empirical Bayesian framework to do both directly. A neighborhood noise model is proposed to reason and infer the Yaroslavsky, bilateral, and modified non-local means filters. An EM+ algorithm is devised to estimate the essential parameter, range variance, via the model fitting to empirical distributions. Finally, we apply this framework to color-image denoising. Experimental results show that the proposed model fits noisy images well and the range variance is estimated successfully. The image quality can also be improved by a proposed recursive fitting and filtering scheme.
Deep LAC: Deep Localization, Alignment and Classification for Fine-Grained Recognition
Authors: Di Lin, Xiaoyong Shen, Cewu Lu, Jiaya Jia
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification

We propose a fine-grained recognition system that incorporates part localization, alignment, and classification in one deep neural network. This is a nontrivial process, as the input to the classification module should be functions that enable back-propagation in constructing the solver. Our major contribution is to propose a valve linkage function(VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system. The VLF can adaptively compromise the errors of classification and alignment when training the LAC model. It in turn helps update localization. The performance on fine-grained object data bears out the effectiveness of our LAC system.
Unconstrained Realtime Facial Performance Capture
Authors: Pei-Lun Hsieh, Chongyang Ma, Jihun Yu, Hao Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We introduce a realtime facial tracking system specifically designed for performance capture in unconstrained settings using a consumer-level RGB-D sensor. Our framework provides uninterrupted 3D facial tracking, even in the presence of extreme occlusions such as those caused by hair, hand-to-face gestures, and wearable accessories. Anyone's face can be instantly tracked and the users can be switched without an extra calibration step. During tracking, we explicitly segment face regions from any occluding parts by detecting outliers in the shape and appearance input using an exponentially smoothed and user-adaptive tracking model as prior. Our face segmentation combines depth and RGB input data and is also robust against illumination changes. To enable continuous and reliable facial feature tracking in the color channels, we synthesize plausible face textures in the occluded regions. Our tracking model is personalized on-the-fly by progressively refining the user's identity, expressions, and texture with reliable samples and temporal filtering. We demonstrate robust and high-fidelity facial tracking on a wide range of subjects with highly incomplete and largely occluded data. Our system works in everyday environments and is fully unobtrusive to the user, impacting consumer AR applications and surveillance.
Blind Optical Aberration Correction by Exploring Geometric and Visual Priors
Authors: Tao Yue, Jinli Suo, Jue Wang, Xun Cao, Qionghai Dai
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Optical aberration widely exists in optical imaging systems, especially in consumer-level cameras. In contrast to previous solutions using hardware compensation or pre-calibration, we propose a computational approach for blind aberration removal from a single image, by exploring various geometric and visual priors. The global rotational symmetry allows us to transform the non-uniform degeneration into several uniform ones by the proposed radial splitting and warping technique. Locally, two types of symmetry constraints, i.e. central symmetry and reflection symmetry are defined as geometric priors in central and surrounding regions, respectively. Furthermore, by investigating the visual artifacts of aberration degenerated images captured by consumer-level cameras, the non-uniform distribution of sharpness across color channels and the image lattice is exploited as visual priors, resulting in a novel strategy to utilize the guidance from the sharpest channel and local image regions to improve the overall performance and robustness. Extensive evaluation on both real and synthetic data suggests that the proposed method outperforms the state-of-the-art techniques.
Ontological Supervision for Fine Grained Classification of Street View Storefronts
Authors: Yair Movshovitz-Attias, Qian Yu, Martin C. Stumpe, Vinay Shet, Sacha Arnoud, Liron Yatziv
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification

Modern search engines receive large numbers of business related, local aware queries. Such queries are best answered using accurate, up-to-date, business listings, that contain representations of business categories. Creating such listings is a challenging task as businesses often change hands or close down. For businesses with street side locations one can leverage the abundance of street level imagery, such as Google Street View, to automate the process. However, while data is abundant, labeled data is not; the limiting factor is creation of large scale labeled training data. In this work, we utilize an ontology of geographical concepts to automatically propagate business category information and create a large, multi label, training data for fine grained storefront classification. Our learner, which is based on the GoogLeNet/inception Deep Convolutional Network architecture and classifies 208 categories, achieves human level accuracy.
Finding Distractors In Images
Authors: Ohad Fried, Eli Shechtman, Dan B. Goldman, Adam Finkelstein
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

We propose a new computer vision task we call "distractor prediction." Distractors are the regions of an image that draw attention away from the main subjects and reduce the overall image quality. Removing distractors --- for example, using in-painting --- can improve the composition of an image. In this work we created two datasets of images with user annotations to identify the characteristics of distractors. We use these datasets to train an algorithm to predict distractor maps. Finally, we use our predictor to automatically enhance images.
Semantic Alignment of LiDAR Data at City Scale
Authors: Fisher Yu, Jianxiong Xiao, Thomas Funkhouser
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Pose Estimation

This paper describes an automatic algorithm for global alignment of LiDAR data collected with Google Street View cars in urban environments. The problem is challenging because global pose estimation techniques (GPS) do not work well in city environments with tall buildings, and local tracking techniques (integration of inertial sensors, structure-from-motion, etc.) provide solutions that drift over long ranges, leading to solutions where data collected over wide ranges is warped and misaligned by many meters.  Our approach to address this problem is to extract ``semantic features'' with object detectors (e.g., for facades, poles, cars, etc.) that can be matched robustly at different scales, and thus are selected for different iterations of an ICP algorithm.  We have implemented an all-to-all, non-rigid, global alignment based on this idea that provides better results than alternatives during experiments with data from large regions of New York, San Francisco, Paris, and Rome.
Query-Adaptive Late Fusion for Image Search and Person Re-Identification
Authors: Liang Zheng, Shengjin Wang, Lu Tian, Fei He, Ziqiong Liu, Qi Tian
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Person Re-Identification

Feature fusion has been proven effective [31, 32] in image search. Typically, it is assumed that the to-be-fused heterogeneous features work well by themselves for the query. However, in a more realistic situation, one does not know in advance whether a feature is effective or not for a given query. As a result, it is of great importance to identify feature effectiveness in a query-adaptive manner.  Towards this goal, this paper proposes a simple yet effective late fusion method at score level. Our motivation is that the sorted score curve exhibits an "L" shape for a good feature, but descends gradually for a bad one (Fig. 1). By approximating score curve's tail with a reference collected on irrelevant data, the effectiveness of a feature can be estimated as negatively related to the area under the normalized score curve.  Experiments are conducted on two image search datasets and one person re-identification dataset. We show that our method is robust to parameter changes, and outperforms two popular fusion schemes, especially on the resistance to bad features. On the three datasets, our results are competitive to the state-of-the-arts.
Filtered Feature Channels for Pedestrian Detection
Authors: Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Optical Flow Estimation, Pedestrian Detection

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis.  Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known result on the Caltech dataset, reaching 93% recall at 1 FPPI.
GRSA: Generalized Range Swap Algorithm for the Efficient Optimization of MRFs
Authors: Kangwei Liu, Junge Zhang, Peipei Yang, Kaiqi Huang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Markov Random Field (MRF) is an important tool and has been widely used in many vision tasks. Thus, the optimization of MRFs is a problem of fundamental importance. Recently, Veskler and Kumar et. al propose the range move algorithms, which are one of the most successful solvers to this problem. However, two problems have limited the applicability of previous range move algorithms: 1) They are limited in the types of energies they can handle (i.e. only truncated convex functions); 2) These algorithms tend to be very slow compared to other graph-cut based algorithms (e.g. a-expansion and ab-swap). In this paper, we propose a generalized range swap algorithm (GRSA) for efficient optimization of MRFs. To address the first problem, we extend the GRSA to arbitrary semimetric energies by restricting the chosen labels in each move so that the energy is submodular on the chosen subset. Furthermore, to feasibly choose the labels satisfying the submodular condition, we provide a sufficient condition of the submodularity. For the second problem, unlike previous range move algorithms which execute the set of all possible range moves, we dynamically obtain the iterative moves by solving a set cover problem, which greatly reduces the number of moves during the optimization. Experiments show that the GRSA offers a great speedup over previous range swap algorithms, while it obtains competitive solutions.
PatchCut: Data-Driven Object Segmentation via Local Shape Transfer
Authors: Jimei Yang, Brian Price, Scott Cohen, Zhe Lin, Ming-Hsuan Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Discovery, Segmentation, Semantic Segmentation

Object segmentation is highly desirable for image understanding and editing. Current interactive tools require a great deal of user effort while automatic methods are usually limited to images of special object categories or with high color contrast. In this paper, we propose a data-driven algorithm that uses examples to break through these limits. As similar objects tend to share similar local shapes, we match query image patches with example images in multiscale to enable local shape transfer. The transferred local shape masks constitute a patch-level segmentation solution space and we thus develop a novel cascade algorithm, PatchCut, for coarse-to-fine object segmentation. In each stage of the cascade, local shape mask candidates are selected to refine the estimated segmentation of the previous stage iteratively with color models. Experimental results on various datasets (Weizmann Horse, Fashionista, Object Discovery and PASCAL) demonstrate the effectiveness and robustness of our algorithm.