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Author |
Diego Porres |
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Title |
Discriminator Synthesis: On reusing the other half of Generative Adversarial Networks |
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Conference Article |
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Year |
2021 |
Publication |
Machine Learning for Creativity and Design, Neurips Workshop |
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Generative Adversarial Networks have long since revolutionized the world of computer vision and, tied to it, the world of art. Arduous efforts have gone into fully utilizing and stabilizing training so that outputs of the Generator network have the highest possible fidelity, but little has gone into using the Discriminator after training is complete. In this work, we propose to use the latter and show a way to use the features it has learned from the training dataset to both alter an image and generate one from scratch. We name this method Discriminator Dreaming, and the full code can be found at this https URL. |
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Virtual; December 2021 |
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NEURIPSW |
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ADAS; 601.365 |
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no |
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Admin @ si @ Por2021 |
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3597 |
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Author |
Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Automatic Ground-truthing using video registration for on-board detection algorithms |
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Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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4389 - 4392 |
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Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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ADAS |
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no |
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ADAS @ adas @ ADS2009 |
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1201 |
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Author |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title |
Harmony Potentials for Joint Classification and Segmentation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS |
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ADAS @ adas @ GBW2010 |
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1296 |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Opponent Colors for Human Detection |
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Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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363-370 |
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Keywords |
Pedestrian Detection; Color; Part Based Models |
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Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
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Las Palmas de Gran Canaria. Spain |
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Springer |
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Berlin Heidelberg |
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J. Vitria; J.M. Sanches; M. Hernandez |
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English |
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English |
Original Title |
Opponent Colors for Human Detection |
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Lecture Notes on Computer Science |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ RVL2011a |
Serial |
1666 |
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Author |
Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez |
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Title |
Action-Based Representation Learning for Autonomous Driving |
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Conference Article |
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Year |
2020 |
Publication |
Conference on Robot Learning |
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Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet). |
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virtual; November 2020 |
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CORL |
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ADAS; 600.118 |
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Admin @ si @ XCP2020 |
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3487 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Virtual Worlds and Active Learning for Human Detection |
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Conference Article |
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Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
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Pages |
393-400 |
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Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
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Alicante, Spain |
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ACM DL |
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New York, NY, USA, USA |
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English |
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English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
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978-1-4503-0641-6 |
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ICMI |
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ADAS |
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yes |
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Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Cool world: domain adaptation of virtual and real worlds for human detection using active learning |
Type |
Conference Article |
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Year |
2011 |
Publication |
NIPS Domain Adaptation Workshop: Theory and Application |
Abbreviated Journal |
NIPS-DA |
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Keywords |
Pedestrian Detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity. |
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Granada, Spain |
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Granada, Spain |
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English |
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English |
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DA-NIPS |
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ADAS |
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no |
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ADAS @ adas @ VLP2011b |
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1756 |
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Author |
Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa |
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Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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Conference Article |
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Year |
2017 |
Publication |
IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point
that minimizes the vacuum force needed to guarantee a grasp.
Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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San Sebastian; Spain; May 2017 |
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ECMSM |
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ADAS; 600.086; 600.118 |
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no |
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Admin @ si @ VIS2017 |
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2917 |
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Author |
Jaume Amores; David Geronimo; Antonio Lopez |
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Title |
Multiple instance and active learning for weakly-supervised object-class segmentation |
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Conference Article |
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2010 |
Publication |
3rd IEEE International Conference on Machine Vision |
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Multiple Instance Learning; Active Learning; Object-class segmentation. |
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In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set. |
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Hong-Kong |
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ICMV |
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ADAS |
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ADAS @ adas @ AGL2010b |
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1429 |
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Author |
Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
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Conference Article |
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2015 |
Publication |
International Conference on Intelligent Robots and Systems |
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2488 - 2495 |
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Visual Learning; Computer Vision; Autonomous Agents |
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In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
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Hamburg; Germany; October 2015 |
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IROS |
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ADAS; 600.076 |
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Call Number |
Admin @ si @ OSL2015 |
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2664 |
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