mtybilly/Instruct2DS huggingface dataset:
Model Recommendation: Generating Object Detectors From Few Samples
Authors: Martial Hebert Yu-Xiong Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Adrien Bartoli, Richard Hartley Abed Malti
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Remi Megret Guillaume Bourmaud
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Jieun Lee, Hyeogjin Lee, Nojun Kwak Minsik Lee
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Xiaoyong Shen, Cewu Lu, Jiaya Jia Di Lin
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Chongyang Ma, Jihun Yu, Hao Li Pei-Lun Hsieh
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Jinli Suo, Jue Wang, Xun Cao, Qionghai Dai Tao Yue
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Qian Yu, Martin C. Stumpe, Vinay Shet, Sacha Arnoud, Liron Yatziv Yair Movshovitz-Attias
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Eli Shechtman, Dan B. Goldman, Adam Finkelstein Ohad Fried
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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.
From Image-Level to Pixel-Level Labeling With Convolutional Networks
Authors: Ronan Collobert Pedro O. Pinheiro
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and naturally fits the Multiple Instance Learning (MIL) framework: every training image is known to have (or not) at least one pixel corresponding to the image class label, and the segmentation task can be rewritten as inferring the pixels belonging to the class of the object (given one image, and its object class).  We propose a Convolutional Neural Network-based model, which is constrained during training to put more weight on pixels which are important for classifying the image. We show that at test time, the model has learned to discriminate the right pixels well enough, such that it performs very well on an existing segmentation benchmark, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object segmentation task by a large margin. We also compare the performance of our model with state of the art fully-supervised segmentation approaches.
Semantic Alignment of LiDAR Data at City Scale
Authors: Jianxiong Xiao, Thomas Funkhouser Fisher Yu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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.
Oriented Edge Forests for Boundary Detection
Authors: Charless C. Fowlkes Sam Hallman
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We present a simple, efficient model for learning boundary detection based on a random forest classifier. Our approach combines (1) efficient clustering of training examples based on a simple partitioning of the space of local edge orientations and (2) scale-dependent calibration of individual tree output probabilities prior to multiscale combination. The resulting model outperforms published results on the challenging BSDS500 boundary detection benchmark. Further, on large datasets our model requires substantially less memory for training and speeds up training time by a factor of 10 over the structured forest model.
Query-Adaptive Late Fusion for Image Search and Person Re-Identification
Authors: Shengjin Wang, Lu Tian, Fei He, Ziqiong Liu, Qi Tian Liang Zheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Rodrigo Benenson, Bernt Schiele Shanshan Zhang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Junge Zhang, Peipei Yang, Kaiqi Huang Kangwei Liu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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: Brian Price, Scott Cohen, Zhe Lin, Ming-Hsuan Yang Jimei Yang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 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.
Illumination and Reflectance Spectra Separation of a Hyperspectral Image Meets Low-Rank Matrix Factorization
Authors: Imari Sato, Yoichi Sato Yinqiang Zheng
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper addresses the illumination and reflectance spectra separation (IRSS) problem of a hyperspectral image captured under general spectral illumination. The huge amount of pixels in a hypersepctral image poses tremendous challenges on computational efficiency, yet in turn offers greater color variety that might be utilized to improve separation accuracy and relax the restrictive subspace illumination assumption in existing works. We show that this IRSS problem can be modeled into a low-rank matrix factorization problem, and prove that the separation is unique up to an unknown scale under the standard low-dimensionality assumption of reflectance. We also develop a scalable algorithm for this separation task that works in the presence of model error and image noise. Experiments on both synthetic data and real images have demonstrated that our separation results are sufficiently accurate, and can benefit some important applications, such as spectra relighting and illumination swapping.
Semantic Part Segmentation Using Compositional Model Combining Shape and Appearance
Authors: Alan L. Yuille Jianyu Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of semantic parts. And we incorporate edge, appearance, and semantic part cues into the compositional model. Given part-level segmentation annotation, we develop a novel algorithm to learn a mixture of compositional models under various poses and viewpoints for certain animal classes. Furthermore, a linear complexity algorithm is offered for efficient inference of the compositional model using dynamic programming. We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method.
A Discriminative CNN Video Representation for Event Detection
Authors: Yi Yang, Alex G. Hauptmann Zhongwen Xu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus of this paper is to effectively leverage deep Convolutional Neural Networks (CNNs) to advance event detection, where only frame level static descriptors can be extracted by the existing CNN toolkits. This paper makes two contributions to the inference of CNN video representation. First, while average pooling and max pooling have long been the standard approaches to aggregating frame level static features, we show that performance can be significantly improved by taking advantage of an appropriate encoding method. Second, we propose using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable. The integration of the two contributions results in a new state-of-the-art performance in event detection over the largest video datasets. Compared to improved Dense Trajectories, which has been recognized as the best video representation for event detection, our new representation improves the Mean Average Precision (mAP) from 27.6% to 36.8% for the TRECVID MEDTest 14 dataset and from 34.0% to 44.6% for the TRECVID MEDTest 13 dataset.
24/7 Place Recognition by View Synthesis
Authors: Relja Arandjelovic, Josef Sivic, Masatoshi Okutomi, Tomas Pajdla Akihiko Torii
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data.
Understanding Image Virality
Authors: Devi Parikh Arturo Deza
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing,  psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata.  We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images,  and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks.  We find that computers perform poorly with low level features, and high level information is critical for  predicting virality. We encode semantic information through relative attributes.  We identify the 5 key visual attributes that correlate with virality.  We create an attribute-based characterization of images that can predict  relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -- better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different `contexts' in which the images are viewed, such  as the influence of neighbouring images, images recently viewed, as well as the image title or  caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.
Book2Movie: Aligning Video Scenes With Book Chapters
Authors: Martin Bauml, Rainer Stiefelhagen Makarand Tapaswi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Film adaptations of novels often visually display in a few shots what is described in many pages of the source novel. In this paper we present a new problem: to align book chapters with video scenes. Such an alignment facilitates finding differences between the adaptation and the original source, and also acts as a basis for deriving rich descriptions from the novel for the video clips. We propose an efficient method to compute an alignment between book chapters and video scenes using matching dialogs and character identities as cues. A major consideration is to allow the alignment to be non-sequential. Our suggested shortest path based approach deals with the non-sequential alignments and can be used to determine whether a video scene was part of the original book. We create a new data set involving two popular novel-to-film adaptations with widely varying properties and compare our method against other text-to-video alignment baselines. Using the alignment, we present a qualitative analysis of describing the video through rich narratives obtained from the novel.
3D Model-Based Continuous Emotion Recognition
Authors: Jiangdong Li, Fengjun Zhang, Yang Li, Hongan Wang Hui Chen
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which provide crucial clues for the enhancement of robustness to overcome large changes including out-of-plane head rotations, fast head motions and partial facial occlusions. To accurately recognize the emotion, a novel random forest-based algorithm which simultaneously integrates two regressions for 3D facial tracking and continuous emotion estimation is constructed. Moreover, via the reconstructed 3D facial model, temporal information and user-independent emotion presentations are also taken into account through our image fusion process. The experimental results show that our algorithm can achieve state-of-the-art result with higher Pearson's correlation coefficient of continuous emotion recognition in real time.
Learning to Rank in Person Re-Identification With Metric Ensembles
Authors: Chunhua Shen, Anton van den Hengel Sakrapee Paisitkriangkrai
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-1 recognition rates from 40% to 50% on the iLIDS benchmark, 16% to 18% on the PRID2011 benchmark, 43% to 46% on the VIPeR benchmark, 34% to 53% on the CUHK01 benchmark and 21% to 62% on the CUHK03 benchmark.
Making Better Use of Edges via Perceptual Grouping
Authors: Yi-Zhe Song, Tao Xiang, Honggang Zhang, Timothy Hospedales, Yi Li, Jun Guo Yonggang Qi
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks. Our grouper formulates edge grouping as a graph partition problem, where a learning to rank method is developed to encode probabilities of candidate edge pairs. In particular, RankSVM is employed for the first time to combine multiple Gestalt principles as cue for edge grouping. Afterwards, an edge grouping based object proposal measure is introduced that yields proposals comparable to state-of-the-art alternatives. We further show how human-like sketches can be generated from edge groupings and consequently used to deliver state-of-the-art sketch-based image retrieval performance. Last but not least, we tackle the problem of freehand human sketch segmentation by utilizing the proposed grouper to cluster strokes into semantic object parts.
Real-Time Joint Estimation of Camera Orientation and Vanishing Points
Authors: Kuk-Jin Yoon Jeong-Kyun Lee
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: The widely-used approach for estimating camera orientation is to use points at infinity, i.e., vanishing points (VPs). By enforcing the orthogonal constraint between the VPs, called the Manhattan world constraint, a drift-free camera orientation estimation can be achieved. However, in practical applications this approach suffers from many spurious parallel line segments or does not perform in non-Manhattan world scenes. To overcome these limitations, we propose a novel method that jointly estimates the VPs and camera orientation based on sequential Bayesian filtering. The proposed method does not require the Manhattan world assumption, and can perform a highly accurate estimation of camera orientation in real time. In addition, in order to enhance the robustness of the joint estimation, we propose a feature management technique that removes false positives of line clusters and classifies newly detected lines. We demonstrate the superiority of the proposed method through an extensive evaluation using synthetic and real datasets and comparison with other state-of-the-art methods.
Sketch-Based 3D Shape Retrieval Using Convolutional Neural Networks
Authors: Le Kang, Yi Li Fang Wang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Retrieving 3D models from 2D human sketches has received considerable attention in the areas of graphics, image retrieval, and computer vision. Almost always in state of the art approaches a large amount of ``best views'' are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features.  We argue that this two stage approach (view selection -- matching) is pragmatic but also problematic because the ``best views'' are subjective and ambiguous, which makes the matching inputs obscure. This imprecise nature of matching further makes it challenging to choose features manually. Instead of relying on the elusive concept of ``best views'' and the hand-crafted features, we propose to define our views using a minimalism approach and learn features for both sketches and views. Specifically, we drastically reduce the number of views to only two predefined directions for the whole dataset. Then, we learn two Siamese Convolutional Neural Networks (CNNs), one for the views and one for the sketches. The loss function is defined on the within-domain as well as the cross-domain similarities. Our experiments on three benchmark datasets demonstrate that our method is significantly better than state of the art approaches, and outperforms them in all conventional metrics.
Salient Object Detection via Bootstrap Learning
Authors: Huchuan Lu, Xiang Ruan, Ming-Hsuan Yang Na Tong
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: We propose a bootstrap learning algorithm for salient object detection in which both weak and strong models are exploited. First, a weak saliency map is constructed based on image priors to generate training samples for a strong model. Second, a strong classifier based on samples directly from an input image is learned to detect salient pixels. Results from multiscale saliency maps are integrated to further improve the detection performance. Extensive experiments on five benchmark datasets demonstrate that the proposed bootstrap learning algorithm performs favorably against the state-of-the-art saliency detection methods. Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.
Towards Open World Recognition
Authors: Terrance Boult Abhijit Bendale
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: With the of advent rich classification models and high computational power visual recognition systems have found many operational applications. Recognition in the real world poses multiple challenges that are not apparent in controlled lab environments. The datasets are dynamic and  novel categories must be continuously detected and then added. At prediction time, a trained system has to deal with myriad unseen categories.  Operational systems require minimum down time, even to learn. To handle these operational issues,  we present the problem of Open World recognition and formally define it. We prove that thresholding sums of monotonically decreasing functions of distances in linearly transformed feature space can balance open space risk and empirical risk. Our theory extends existing algorithms for open world recognition.  We present a protocol for evaluation of open world recognition systems. We present the Nearest Non-Outlier (NNO) algorithm which  evolves model efficiently,  adding object categories incrementally while detecting outliers and  managing open space risk.       We perform experiments on the ImageNet dataset with 1.2M+  images to validate the effectiveness of our method on large scale visual recognition tasks.  NNO consistently yields superior results on open world recognition.
Data-Driven 3D Voxel Patterns for Object Category Recognition
Authors: Wongun Choi, Yuanqing Lin, Silvio Savarese Yu Xiang
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark and the outdoor-scene dataset. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.
3D ShapeNets: A Deep Representation for Volumetric Shapes
Authors: Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao Zhirong Wu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: 3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.
Robust Image Alignment With Multiple Feature Descriptors and Matching-Guided Neighborhoods
Authors: Yen-Yu Lin, Yung-Yu Chuang Kuang-Jui Hsu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: This paper addresses two issues hindering the advances in accurate image alignment. First, the performance of descriptor-based approaches to image alignment relies on the chosen descriptor, but the optimal descriptor typically varies from image to image, or even pixel to pixel. Second, the neighborhood structure for smoothness enforcement is usually predefined before alignment. However, object boundaries are often better discovered during alignment. The proposed approach tackles the two issues by adaptive descriptor selection and dynamic neighborhood construction. Specifically, we associate each pixel to be aligned with an affine transformation, and integrate the learning of the pixel-specific transformations into image alignment. The transformations serve as the common domain for descriptor fusion, since the local consensus of each descriptor can be estimated by accessing the corresponding affine transformation. It allows us to pick the most plausible descriptor for aligning each pixel. On the other hand, more object-aware neighborhoods can be produced by referencing the consistency between the learned affine transformations of neighboring pixels. The promising results on popular image alignment benchmarks manifests the effectiveness of our approach.
Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
Authors: Ben Klein, Emma Taborsky, Austin Blanton, Jordan Cheney, Kristen Allen, Patrick Grother, Alan Mah, Anil K. Jain Brendan F. Klare
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search)  and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.
Depth From Shading, Defocus, and Correspondence Using Light-Field Angular Coherence
Authors: Pratul P. Srinivasan, Jitendra Malik, Szymon Rusinkiewicz, Ravi Ramamoorthi Michael W. Tao
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Light-field cameras are now used in consumer and industrial applications. Recent papers and products have demonstrated practical depth recovery algorithms from a passive single-shot capture. However, current light field capture devices have narrow baselines and constrained spatial resolution; therefore, the accuracy of depth recovery is limited, requiring heavy regularization and producing planar depths that do not resemble the actual geometry. Using shading information is essential to improve the shape estimation. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth.  Light-field cameras are able to capture both spatial and angular data, suitable for refocusing. By locally refocusing each spatial pixel to its respective estimated depth, we produce an all-in-focus image where all viewpoints converge onto a point in the scene. Therefore, the angular pixels have angular coherence, which exhibits three properties: photo consistency, depth consistency, and shading consistency. We propose a new framework that uses angular coherence to optimize depth and shading. The optimization framework estimates both general lighting in natural scenes and shading to improve depth regularization. Our method outperforms current state-of-the-art light-field depth estimation algorithms in multiple scenarios, including real images.
New Insights Into Laplacian Similarity Search
Authors: Zhenguo Li, Shih-Fu Chang Xiao-Ming Wu
Published in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (Main Conference Track), 2015

Abstract: Graph-based computer vision applications rely critically on similarity metrics which compute the pairwise similarity between any pair of vertices on graphs. This paper investigates the fundamental design of commonly used similarity metrics, and provides new insights to guide their use in practice. In particular, we introduce a family of similarity metrics in the form of (L+\alpha\Lambda)^{-1}, where L is the graph Laplacian, \Lambda is a positive diagonal matrix acting as a regularizer, and \alpha is a positive balancing factor. Such metrics respect graph topology when \alpha is small, and reproduce well-known metrics such as hitting times and the pseudo-inverse of graph Laplacian with different regularizer \Lambda.     This paper is the first to analyze the important impact of selecting \Lambda in retrieving the local cluster from a seed. We find that different \Lambda can lead to surprisingly complementary behaviors: \Lambda = D (degree matrix) can reliably extract the cluster of a query if it is sparser than surrounding clusters, while \Lambda = I (identity matrix) is preferred if it is denser than surrounding clusters. Since in practice there is no reliable way to determine the local density in order to select the right model, we propose a new design of \Lambda that automatically adapts to the local density. Experiments on image retrieval verify our theoretical arguments and confirm the benefit of the proposed metric. We expect the insights of our theory to provide guidelines for more applications in computer vision and other domains.