Multimodal news article analysis

The intersection of Computer Vision and Natural Language Processing has been a hot topic of research in recent years, with results that were unthinkable only a few years ago. In view of this progress, we want to highlight online news articles as a potential next step for this area of research. The rich interrelations of text, tags, images or videos, as well as a vast corpus of general knowledge are an exciting benchmark for high-capacity models such as the deep neural networks. In this paper we present a series of tasks and baseline approaches to leverage corpus such as the BreakingNews dataset.Peer ReviewedPostprint (author's final draft

Large-scale image classification using ensembles of nested dichotomies

Many techniques to reduce the cost at test time in large-scale problems involve a hierarchical organization of classifiers, but are either too expensive to learn or degrade the classification performance. Conversely, in this work we show that using ensembles of randomized hierarchical decompositions of the original problem can both improve the accuracy and reduce the computational complexity at test time. The proposed method is evaluated in the ImageNet Large Scale Visual Recognition Challenge’10, with promising results.Peer ReviewedPostprint (author’s final draft

Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation

Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection, but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation. In this work we evaluate the performance of Random Forest on the ImageNet dataset, and compare it to standard approaches in the state-of-the-art.Peer ReviewedPostprint (author’s final draft

A 3D descriptor to detect task-oriented grasping points in clothing

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.Peer ReviewedPostprint (author's final draft

Breakingnews: article annotation by image and text processing

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.Peer ReviewedPostprint (author's final draft

Characterization of textile grasping experiments

Grasping highly deformable objects, like textiles, is an emerging area of research that involves both percep- tion and manipulation abilities. As new techniques appear, it becomes essential to design strategies to compare them. However, this is not an easy task, since the large state-space of textile objects explodes when coupled with the variability of grippers, robotic hands and robot arms performing the manipulation task. This high variability makes it very difficult to design experiments to evaluate the performance of a system in a repeatable way and compare it to others. We propose a framework to allow the comparison of different grasping methods for textile objects. Instead of measuring each component separately, we there- fore propose a methodology to explicitly measure the vision- manipulation correlation by taking into account the throughput of the actions. Perceptions of deformable objects should be grouped into different clusters, and the different grasping actions available should be tested for each perception type to obtain the action-perception success ratio. This characterization potentially allows to compare very different systems in terms of specialized actions , perceptions or widely useful actions , along with the cost of performing each action. We will also show that this categorization is useful in manipulation planning of deformable objects.Peer ReviewedPostprint (author’s final draft

Single image 3D human pose estimation from noisy observations

Markerless 3D human pose detection from a single image is a severely underconstrained problem because different 3D poses can have similar image projections. In order to handle this ambiguity, current approaches rely on prior shape models that can only be correctly adjusted if 2D image features are accurately detected. Unfortunately, although current 2D part detector algorithms have shown promising results, they are not yet accurate enough to guarantee a complete disambiguation of the 3D inferred shape. In this paper, we introduce a novel approach for estimating 3D human pose even when observations are noisy. We propose a stochastic sampling strategy to propagate the noise from the image plane to the shape space. This provides a set of ambiguous 3D shapes, which are virtually undistinguishable from their image projections. Disambiguation is then achieved by imposing kinematic constraints that guarantee the resulting pose resembles a 3D human shape. We validate the method on a variety of situations in which state-of-the-art 2D detectors yield either inaccurate estimations or partly miss some of the body parts.Preprin

Semantic tuples for evaluation of image sentence generation

The automatic generation of image captions has received considerable attention. The problem of evaluating caption generation systems, though, has not been that much explored. We propose a novel evaluation approach based on comparing the underlying visual semantics of the candidate and ground-truth captions. With this goal in mind we have defined a semantic representation for visually descriptive language and have augmented a subset of the Flickr-8K dataset with semantic annotations. Our evaluation metric (BAST) can be used not only to compare systems but also to do error analysis and get a better understanding of the type of mistakes a system does. To compute BAST we need to predict the semantic representation for the automatically generated captions. We use the Flickr-ST dataset to train classifiers that predict STs so that evaluation can be fully automated.Peer ReviewedPostprint (author's final draft

Object recognition for autonomous objects: comparison of two approaches

Report for the scientific sojourn at the Swiss Federal Institute of Technology Zurich, Switzerland, between September and December 2007. In order to make robots useful assistants for our everyday life, the ability to learn and recognize objects is of essential importance. However, object recognition in real scenes is one of the most challenging problems in computer vision, as it is necessary to deal with difficulties. Furthermore, in mobile robotics a new challenge is added to the list: computational complexity. In a dynamic world, information about the objects in the scene can become obsolete before it is ready to be used if the detection algorithm is not fast enough. Two recent object recognition techniques have achieved notable results: the constellation approach proposed by Lowe and the bag of words approach proposed by Nistér and Stewénius. The Lowe constellation approach is the one currently being used in the robot localization project of the COGNIRON project. This report is divided in two main sections. The first section is devoted to briefly review the currently used object recognition system, the Lowe approach, and bring to light the drawbacks found for object recognition in the context of indoor mobile robot navigation. Additionally the proposed improvements for the algorithm are described. In the second section the alternative bag of words method is reviewed, as well as several experiments conducted to evaluate its performance with our own object databases. Furthermore, some modifications to the original algorithm to make it suitable for object detection in unsegmented images are proposed

Multimodal news article analysis

The intersection of Computer Vision and Natural Language Processing has been a hot topic of research in recent years, with results that were unthinkable only a few years ago. In view of this progress, we want to highlight online news articles as a potential next step for this area of research. The rich interrelations of text, tags, images or videos, as well as a vast corpus of general knowledge are an exciting benchmark for high-capacity models such as the deep neural networks. In this paper we present a series of tasks and baseline approaches to leverage corpus such as the BreakingNews dataset.Peer Reviewe