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Collective Matrix Factorization (CMF) is a popular model for the joint matrix completion task, but limited by its strong assumption that all matrices share the same low rank structure. Recently, a promising alternative model was proposed with a relaxed assumption that matrix structures are partially shared. We refer it as Partially Collective Matrix Factorization (P-CMF). This paper presents a first...
We introduce a recurrent neural network architecture for automated road surface wetness detection from audio of tire-surface interaction. The robustness of our approach is evaluated on 785,826 bins of audio that span an extensive range of vehicle speeds, noises from the environment, road surface types, and pavement conditions including international roughness index (IRI) values from 25 in/mi to 1400...
Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks. While such model-free methods do achieve considerable performance, they often ignore the structure of task. We present a more natural representation of the solutions to Reinforcement Learning (RL) problems, within 3...
This paper addresses a problem in which we learn a regression model from sets of training data. Each of the sets has an only single label, and only one of the training data in the set reflects the label. This is particularly the case when the label is attached to a group of data, such as time-series data. The label is not attached to the point of the sequence but rather attached to particular time...
Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality discriminative feature learning, the downside of CNNs is the lack of explicit structure in features, which often leads to overfitting, absence of reconstruction from partial observations and limited generative abilities. Explicit...
We present Deep Sparse-coded Network (DSN), a deep architecture based on multilayer sparse coding. It has been considered difficult to learn a useful feature hierarchy by stacking sparse coding layers in a straightforward manner. The primary reason is the modeling assumption for sparse coding that takes in a dense input and yields a sparse output vector. Applying a sparse coding layer on the output...
This paper presents fine-tuned CNN features for person re-identification. Recently, features extracted from top layers of pre-trained Convolutional Neural Network (CNN) on a large annotated dataset, e.g., ImageNet, have been proven to be strong off-the-shelf descriptors for various recognition tasks. However, large disparity among the pre-trained task, i.e., ImageNet classification, and the target...
An automatic lookup tool, which matches and retrieves similar floorplans from a large repository of digitized architectural floorplans can prove to be of immense help for the architects while designing new projects. In this paper, we have proposed a framework for the matching and retrieval of similar architectural floorplans under the query by example paradigm. We propose a room layout segmentation...
Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such...
Labeling problems are finding increasing applications to optimization problems. They usually get realized into linear or quadratic optimization problems, which are inefficient for large graphs. In this paper we propose an efficient primal-dual solution, MLPD, for a family of labeling problems. We apply this algorithm to the analysis of immune repertoires, and compare it against our baseline approach...
The quality of user experience online is affected by the relevance and placement of advertisements. We propose a new system for selecting and displaying visual advertisements in image search result sets. Our method compares the visual similarity of candidate ads to the image search results and selects the most visually similar ad to be displayed. The method further selects an appropriate location...
The distance set is known to be a versatile local descriptor of shape. As this is simply a set of ordinary distances between sample points on a shape, it is easy to construct and use. More importantly, it remains invariant under many settings and deformations, unlike other typical descriptors. However, in shape matching with distance sets, there is a tradeoff between performance and computational...
This paper addresses the problem of automatic machine analysis based severity scoring of psoriasis skin disease. Three different disease parameters namely, erythema, scaling and induration are considered for such severity grading. Given an image containing a psoriatic plaque the task is to predict severity scores for all the three parameters. This paper presents a novel deep CNN based architecture...
Clinical studies have established the importance of morphologic measurements of intracranial aneurysm size, neck width, aspect ratio and other shape indices for the assessment of the risk of rupture and selection of the best treatment option. Obtaining these morphologic measurements requires segmentation of vascular structures in an angiographic image, reconstruction of a 3D vascular surface mesh,...
Computer-Aided Diagnosis (CAD) has witnessed a rapid growth over the past decade, providing a variety of automated tools for the analysis of medical images. In surgical pathology, such tools enhance the diagnosing capabilities of pathologists by allowing them to review and diagnose a larger number of cases daily. Geared towards developing such tools, the main goal of this paper is to identify useful...
We propose a convex optimization approach for multi-label feature selection. The effective feature subset can be obtained through finding a global optima of a convex objective function for multi-label feature selection. However conventional greedy approaches are prone to suboptimal result. In this paper, the mathematical procedures and considerations for the optimization approach are presented for...
Video summarization is useful to find a concise representation of the original video, nevertheless its evaluation is somewhat challenging. This paper proposes a simple and efficient method for precisely evaluating the video summaries produced by the existing techniques. This method includes two steps. The first step is to establish a set of matched frames between automatic summary (AT) and the ground...
Although both feature dependencies and label dependencies are crucial for facial action unit (AU) recognition, little work addresses them simultaneously till now. To address this limitation, we propose a 4-layer Restrict Boltzmann Machine (RBM) to simultaneously capture feature level and AU level dependencies to recognize multiple AUs. Specifically, the bottom two layers of the RBM model capture dependencies...
Depth recovery from a light-field camera is an essential and interesting problem. One of its most challenges is to get accurate estimation for the depth discontinuities and occluded regions. We propose a simple and efficient solution with a cascade occlusion culling filter. It is a cascade processing corresponding to the different manifestations of occlusions at ray-level, pixel-level and image-level...
In this paper, we propose a saliency detection model for RGB-D images based on the contrasting features of color and depth within a Bayesian framework. The depth feature map is extracted based on superpixel contrast computation with spatial priors. We model the depth saliency map by approximating the density of depth-based contrast features using a Gaussian distribution. Similar to the depth saliency...
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