pwc-archive/papers-with-abstracts huggingface dataset:
Effective Learning-Based Illuminant Estimation Using Simple Features
Authors: Dongliang Cheng, Brian Price, Scott Cohen, Michael S. Brown
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Color Constancy

Illumination estimation is the process of determining the chromaticity of the illumination in an imaged scene in order to remove undesirable color casts through white-balancing.   While computational color constancy is a well-studied topic in computer vision, it remains challenging due to the ill-posed nature of the problem.   One class of techniques relies on low-level statistical information in the image color distribution and works under various assumptions (e.g. Grey-World, White-Patch, etc). These methods have an advantage that they are simple and fast, but often do not perform well.  More recent state-of-the-art methods employ learning-based techniques that produce better results, but often rely on complex features and have long evaluation and training times.  In this paper, we present a learning-based method based on four simple color features and show how to use this with an ensemble of regression trees to estimate the illumination.  We demonstrate that our approach is not only faster than existing learning-based methods in terms of both evaluation and training time, but also gives the best results reported to date on modern color constancy data sets.
PAIGE: PAirwise Image Geometry Encoding for Improved Efficiency in Structure-From-Motion
Authors: Johannes L. Schonberger, Alexander C. Berg, Jan-Michael Frahm
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent significant effort on improving the efficiency of this stage. In this paper, we present a comprehensive overview of various state-of-the-art methods, evaluating and analyzing their performance. Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification. PAIGE achieves state-of-the-art performance and integrates well into existing Structure-from-Motion pipelines.
Dense, Accurate Optical Flow Estimation With Piecewise Parametric Model
Authors: Jiaolong Yang, Hongdong Li
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Optical Flow Estimation

This paper proposes a simple method for estimating dense and accurate optical flow field. It revitalizes an early idea of piecewise parametric flow model. A key innovation is that, we fit a flow field piecewise to a variety of parametric models, where the domain of each piece (i.e., each piece's shape, position and size) is determined adaptively, while at the same time maintaining a global inter-piece flow continuity constraint. We achieve this by a multi-model fitting scheme via energy minimization. Our energy takes into account both the piecewise constant model assumption and the flow field continuity constraint, enabling the proposed method to effectively handle both homogeneous motions and complex motions. The experiments on three public optical flow benchmarks (KITTI, MPI Sintel, and Middlebury) show the superiority of our method compared with the state of the art: it achieves top-tier performances on all the three benchmarks.
Single-Image Estimation of the Camera Response Function in Near-Lighting
Authors: Pedro Rodrigues, Joao P. Barreto
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

The camera response function (CRF) relates quantised image pixel values with physical incoming light. This paper describes a method to estimate the CRF from a single image of a general two-coloured surface for which the albedo ratio between the coloured regions is known a priori. While other radiometric calibration methods either use multiple frames or require the light to be infinitely distant, the algorithm herein proposed makes no assumptions about lighting conditions and can handle cameras with strong vignetting. Although the approach is generic, in the sense that can be applied to any camera system, the method is particularly well suited for determining the CRF of near-lighting endoscopes in the operating room. This is a very pertinent problem for which no practical, effective solutions have been proposed. The robustness, repeatability, and accuracy of the algorithm is experimentally validated in real images acquired with different endoscopic set-ups.
Multispectral Pedestrian Detection: Benchmark Dataset and Baseline
Authors: Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, In So Kweon
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Pedestrian Detection

With the increasing interest in pedestrian detection, pedestrian datasets have also been the subject of research in the past decades. However, most existing datasets focus on a color channel, while a thermal channel is helpful for detection even in a dark environment. With this in mind, we propose a multispectral pedestrian dataset which provides well aligned color-thermal image pairs, captured by beam splitter-based special hardware. The color-thermal dataset is as large as previous color-based datasets and provides dense annotations including temporal correspondences. With this dataset, we introduce multispectral ACF, which is an extension of aggregated channel features (ACF) to simultaneously handle color-thermal image pairs. Multispectral ACF reduces the average miss rate of ACF by 15%, and achieves another breakthrough in the pedestrian detection task.
A Low-Dimensional Step Pattern Analysis Algorithm With Application to Multimodal Retinal Image Registration
Authors: Jimmy Addison Lee, Jun Cheng, Beng Hai Lee, Ee Ping Ong, Guozhen Xu, Damon Wing Kee Wong, Jiang Liu, Augustinus Laude, Tock Han Lim
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Registration

Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are often being exploited, they do not work very well upon unhealthy multimodal images with severe diseases. Additionally, the descriptors demand high dimensionality to adequately represent the features of interest. The higher the dimensionality, the greater the consumption of resources (e.g. memory space). To this end, this paper introduces a novel registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively align unhealthy multimodal image pairs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. This method is insensitive to intensity changes, and produces uniformly distributed features and high repeatability across the image domain. The algorithm continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are robust to non-linear intensity changes, which are well-suited for multimodal retinal image registration. Apart from its low dimensionality, the LoSPA algorithm achieves about two-fold higher success rate in multimodal registration on the dataset of severe retinal diseases when compared to the top score among state-of-the-art algorithms.
Bilinear Heterogeneous Information Machine for RGB-D Action Recognition
Authors: Yu Kong, Yun Fu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Action Recognition, Temporal Action Localization

This paper proposes a novel approach to action recognition from RGB-D cameras, in which depth features and RGB visual features are jointly used. Rich heterogeneous RGB and depth data are effectively compressed and projected to a learned shared space, in order to reduce noise and capture useful information for recognition. Knowledge from various sources can then be shared with others in the learned space to learn cross-modal features. This guides the discovery of valuable information for recognition. To capture complex spatiotemporal structural relationships in visual and depth features, we represent both RGB and depth data in a matrix form. We formulate the recognition task as a low-rank bilinear model composed of row and column parameter matrices. The rank of the model parameter is minimized to build a low-rank classifier, which is beneficial for improving the generalization power. The proposed method is extensively evaluated on two public RGB-D action datasets, and achieves state-of-the-art results. It also shows promising results if RGB or depth data are missing in training or testing procedure.
SALICON: Saliency in Context
Authors: Ming Jiang, Shengsheng Huang, Juanyong Duan, Qi Zhao
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Saliency Prediction

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention. This paper presents a new method to collect large-scale human data during natural explorations on images. While current datasets present a rich set of images and task-specific annotations such as category labels and object segments, this work focuses on recording and logging how humans shift their attention during visual exploration. The goal is to offer new possibilities to (1) complement task-specific annotations to advance the ultimate goal in visual understanding, and (2) understand visual attention and learn saliency models, all with human attentional data at a much larger scale.  We designed a mouse-contingent multi-resolutional paradigm based on neurophysiological and psychophysical studies of peripheral vision, to simulate the natural viewing behavior of humans. The new paradigm allowed using a general-purpose mouse instead of an eye tracker to record viewing behaviors, thus enabling large-scale data collection. The paradigm was validated with controlled laboratory as well as large-scale online data. We report in this paper a proof-of-concept SALICON dataset of human "free-viewing" data on 10,000 images from the Microsoft COCO (MS COCO) dataset with rich contextual information. We evaluated the use of the collected data in the context of saliency prediction, and demonstrated them a good source as ground truth for the evaluation of saliency algorithms.
Weakly Supervised Object Detection With Convex Clustering
Authors: Hakan Bilen, Marco Pedersoli, Tinne Tuytelaars
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Object, object-detection, Object Detection, Weakly Supervised Object Detection

Weakly supervised object detection, is a challenging task, where the training procedure involves learning at the same time both, the model appearance and the object location in each image. The classical approach to solve this problem is to consider the location of the object of interest in each image as a latent variable and minimize the loss generated by such latent variable during learning.  However, as learning appearance and localization are two interconnected tasks, the optimization is not convex and the procedure can easily get stuck in a poor local minimum, the algorithm "misses" the object in some images. In this paper, we help the optimization to get close to the global minimum by enforcing a "soft" similarity between each possible location in the image and a reduced set of "exemplars", or clusters, learned with a convex formulation in the training images.  The help is effective because it comes from a different and smooth source of information that is not directly connected with the main task.  Results show that our method improves a strong baseline based on convolutional neural network features by more than 4 points without any additional features or extra computation at testing time but only adding a small increment of the training time due to the convex clustering.
Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database
Authors: Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Diagnostic, Retrieval, Sentence

Despite tremendous progress in computer vision, effective learning on very large-scale (>100K patients) medical image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system. Instead of using full 3D medical volumes, we focus on a collection of representative ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector labels. Our system interleaves between unsupervised learning (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demonstrated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their categorization, embedded vector labels and sentence descriptions can be harnessed to alleviate the deep learning "data-hungry" obstacle in the medical domain.
Learning Semantic Relationships for Better Action Retrieval in Images
Authors: Vignesh Ramanathan, Cong-Cong Li, Jia Deng, Wei Han, Zhen Li, Kunlong Gu, Yang song, Samy Bengio, Charles Rosenberg, Li Fei-Fei
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval

Human actions capture a wide variety of interactions between people and objects. As a result, the set of possible actions is extremely large and it is difficult to obtain sufficient training examples for all actions. However, we could compensate for this sparsity in supervision by leveraging the rich semantic relationship between different actions. A single action is often composed of other smaller actions and is exclusive of certain others. We need a method which can reason about such relationships and extrapolate unobserved actions from known actions. Hence, we propose a novel neural network framework which jointly extracts the relationship between actions and uses them for training better action retrieval models. Our model incorporates linguistic, visual and logical consistency based cues to effectively identify these relationships. We train and test our model on a largescale image dataset of human actions. We show a significant improvement in mean AP compared to different baseline methods including the HEX-graph approach from Deng et al.
Depth and Surface Normal Estimation From Monocular Images Using Regression on Deep Features and Hierarchical CRFs
Authors: Bo Li, Chunhua Shen, Yuchao Dai, Anton Van Den Hengel, Mingyi He
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: regression, Surface Normal Estimation

Predicting the depth (or surface normal) of a scene from single monocular color images is a challenging task. This paper tackles this challenging and essentially under-determined problem by regression on deep convolutional neural network (DCNN) features, combined with a post-processing refining step using conditional random fields(CRF). Our framework works at two levels, super-pixel level and pixel level. First, we design a DCNN model to learn the mapping from multi-scale image patches to depth or surface normal values at the super-pixel level. Second, the estimated super-pixel depth or surface normal is refined to the pixel level by exploiting various potentials on the depth or surface normal map, which includes a data term, a smoothness term among super-pixels and an auto-regression term characterizing the local structure of the estimation map. The inference problem can be efficiently solved because it admits a closed-form solution. Experiments on the Make3D and NYU Depth V2 datasets show competitive results compared with recent state-of-the-art methods.
Discriminative Shape From Shading in Uncalibrated Illumination
Authors: Stephan R. Richter, Stefan Roth
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency

Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets.
Multi-Manifold Deep Metric Learning for Image Set Classification
Authors: Jiwen Lu, Gang Wang, Weihong Deng, Pierre Moulin, Jie zhou
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, General Classification, Metric Learning

In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations. Motivated by the fact that manifold can be effectively used to model the nonlinearity of samples in each image set and deep learning has demonstrated superb capability to model the nonlinearity of samples, we propose a MMDML method to learn multiple sets of nonlinear transformations, one set for each object class, to nonlinearly map multiple sets of image instances into a shared feature subspace, under which the manifold margin of different class is maximized, so that both discriminative and class-specific information can be exploited, simultaneously. Our method achieves the state-of-the-art performance on five widely used datasets.
Target Identity-Aware Network Flow for Online Multiple Target Tracking
Authors: Afshin Dehghan, Yicong Tian, Philip H. S. Torr, Mubarak Shah
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Multiple Object Tracking, object-detection, Object Detection, Object Tracking

In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously. Our method allows us to overcome the confinements of data association based MOT approaches; where the performance is dependent on the object detection results provided at input level. At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip. The inference of our structured learning is done through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The proposed Lagrangian relaxation optimization finds the high quality solution to the network. During optimization a soft spatial constraint is enforced between the nodes of the graph which helps reducing the ambiguity caused by nearby targets with similar appearance in crowded scenarios. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. Our experiments involve challenging yet distinct datasets and show that our method can achieve results better than the state-of-art.
Adaptive As-Natural-As-Possible Image Stitching
Authors: Chung-Ching Lin, Sharathchandra U. Pankanti, Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Stitching

The goal of image stitching is to create natural-looking mosaics free of artifacts that may occur due to relative camera motion, illumination changes, and optical aberrations. In this paper, we propose a novel stitching method, that uses a smooth stitching field over the entire target image, while accounting for all the local transformation variations. Computing the warp is fully automated and uses a combination of local homography and global similarity transformations, both of which are estimated with respect to the target. We mitigate the perspective distortion in the non-overlapping regions by linearizing the homography and slowly changing it to the global similarity. The proposed method is easily generalized to multiple images, and allows one to automatically obtain the best perspective in the panorama. It is also more robust to parameter selection, and hence more automated compared with state-of-the-art methods. The benefits of this method are demonstrated using a variety of challenging cases.
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification
Authors: Gong Cheng, Junwei Han, Lei Guo, Tianming Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification, object-detection, Object Detection, Scene Classification

Part model-based methods have been successfully applied to object detection and scene classification and have achieved state-of-the-art results. More recently the "sparselets" work [1-3] were introduced to serve as a universal set of shared basis learned from a large number of part detectors, resulting in notable speedup. Inspired by this framework, in this paper, we propose a novel scheme to train more effective sparselets with a coarse-to-fine framework. Specifically, we first train coarse sparselets to exploit the redundancy existing among part detectors by using an unsupervised single-hidden layer auto-encoder. Then, we simultaneously train fine sparselets and activation vectors using a supervised single-hidden-layer neural network, in which sparselets training and discriminative activation vectors learning are jointly embedded into a unified framework. In order to adequately explore the discriminative information hidden in the part detectors and to achieve sparsity, we propose to optimize a new discriminative objective function by imposing L0-norm sparsity constraint on the activation vectors. By using the proposed framework, promising results for multi-class object detection and scene classification are achieved on PASCAL VOC 2007, MIT Scene-67, and UC Merced Land Use datasets, compared with the existing sparselets baseline methods.