Search results for: Lorenzo Torresani
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Computer Vision and Image Understanding > 2017 > 164 > C > 111-123
In this work we focus on the problem of colorization for image compression. Since color information occupies a large proportion of the total storage size of an image, a method that can predict accurate color from its grayscale version can produce a dramatic reduction in image file size. But colorization for compression poses several challenges. First, while colorization for artistic purposes simply...
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Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resolution inside the deep layers. This leads to predictions with poor localization around the boundaries. Prior work has attempted to address this issue by post-processing predictions with CRFs or MRFs. But such models often...
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Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. The process of characterization is time-intensive and requires years of specialized medical training. In this work, we propose a deep-learning-based image analysis approach that not only can accurately classify different types...
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The state-of-the-art in semantic segmentation is currently represented by fully convolutional networks (FCNs). However, FCNs use large receptive fields and many pooling layers, both of which cause blurring and low spatial resolution in the deep layers. As a result FCNs tend to produce segmentations that are poorly localized around object boundaries. Prior work has attempted to address this issue in...
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Over the last few years deep learning methods have emerged as one of the most prominent approaches for video analysis. However, so far their most successful applications have been in the area of video classification and detection, i.e., problems involving the prediction of a single class label or a handful of output variables per video. Furthermore, while deep networks are commonly recognized as the...
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We propose a new tracking technique that is able to capture non-rigid motion by exploiting a space-time rank constraint. Most tracking methods use a prior model in order to deal with challenging local features. The model usually has to be trained on carefully hand-labeled example data before the tracking algorithm can be used. Our new model-free tracking technique can overcome such limitations. This...
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In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance...
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Lecture Notes in Computer Science > Computer Vision – ECCV 2010 > Spotlights and Posters M2 > 776-789
We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition...
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We present a robust framework for estimating non-rigid 3D shape and motion in video sequences. Given an input video sequence, and a user-specified region to reconstruct, the algorithm automatically solves for the 3D time-varying shape and motion of the object, and estimates which pixels are outliers, while learning all system parameters, including a PDF over non-rigid deformations. There are no user-tuned...
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This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i.e., without using any ground-truth bounding boxes for training. The key idea is to analyze the change in the recognition scores when artificially masking out different regions of...
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In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map has been computed. We express a global (coarse) depth map of an image as a linear combination of a depth basis learned from training examples. The depth basis captures spatial and statistical regularities and reduces...
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International Journal of Computer Vision > 2016 > 119 > 3 > 239-253
In this paper we present EXMOVES—learned exemplar-based features for efficient recognition and analysis of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input video sequences. Each movement classifier is a simple exemplar-SVM trained on low-level features, i.e., an SVM learned using a single annotated...
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Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network...
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We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets, 2) A homogeneous architecture with small 3x3x3 convolution kernels in all layers is...
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IEEE Transactions on Pattern Analysis and Machine Intelligence > 2015 > 37 > 6 > 1274 - 1285
Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content of the pictures appearing in a Web page can be used to enrich the semantic description of...
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Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional...
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This paper addresses the problem of image annotation using a combination of visual and semantic information. Our model involves two stages: a Nearest Neighbor computation and a tag transfer stage that collects the final annotations. For the latter stage, several algorithms have been implemented in the past using labels' information or including implicitly some visual features. In this paper we propose...
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AutoCaption is a system that helps a smartphone user generate a caption for their photos. It operates by uploading the photo to a cloud service where a number of parallel modules are applied to recognize a variety of entities and relations. The outputs of the modules are combined to generate a large set of candidate captions, which are returned to the phone. The phone client includes a convenient...
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Pattern Recognition > 2014 > 47 > 3 > 1523-1534
Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated...
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IEEE Transactions on Pattern Analysis and Machine Intelligence > 2014 > 36 > 10 > 1988 - 2001
This paper describes compact image descriptors enabling accurate object categorization with linear classification models, which offer the advantage of being efficient to both train and test. The shared property of our descriptors is the use of classifiers to produce the features of each image. Intuitively, these classifiers evaluate the presence of a set of basis classes inside the image. We first...
