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Author |
Adriana Romero; Petia Radeva; Carlo Gatta |
![download PDF file pdf](img/file_PDF.gif)
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No more meta-parameter tuning in unsupervised sparse feature learning |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2014 |
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Arxiv |
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CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well. |
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MILAB; LAMP; 600.079 |
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Admin @ si @ RRG2014 |
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2471 |
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Author |
Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
3d Pedestrian Detection via Random Forest |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2014 |
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European Conference on Computer Vision |
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231-238 |
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Pedestrian Detection |
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Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications. |
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Zurich; suiza; September 2014 |
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ECCV-Demo |
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ADAS; 600.076 |
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Admin @ si @ VRR2014 |
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2570 |
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Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
An Effective Solution to Double Counting Problem in Human Pose Estimation |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2015 |
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Pose estimation; double counting problem; mix-ture of parts Model |
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The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.
An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015]. |
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ISE; 600.078 |
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Admin @ si @ GHG2015 |
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2590 |
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Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2015 |
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Arxiv |
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Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness. |
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MILAB |
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no |
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Admin @ si @ ADR2015b |
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2713 |
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Author |
Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2016 |
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Arxiv |
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Domain Adaptation; Pedestrian detection; Random Forest |
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Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved. |
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ADAS |
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ADAS @ adas @ MVJ2016 |
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2868 |
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Author |
Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven |
![download PDF file pdf](img/file_PDF.gif)
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Title |
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2017 |
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Arxiv |
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arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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HuPBA; ISE; 600.098; 600.119 |
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Admin @ si @ GGM2017 |
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2932 |
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Author |
Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2023 |
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Arxiv |
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107241 |
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This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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IAM |
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Admin @ si @ CCG2023 |
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3855 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2017 |
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Open Living Lab Days Report |
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Krakow; August 2017 |
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MV; no menciona;SIAI |
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Admin @ si @ Vil2017b |
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3033 |
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Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
![download PDF file pdf](img/file_PDF.gif)
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Title |
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDT2018 |
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3085 |
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Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier |
![goto web page url](img/www.gif)
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Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field. |
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HUPBA |
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Admin @ si @ JKS2018 |
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3095 |
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Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
![find record details (via OpenURL) openurl](img/xref.gif)
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A picture is worth a thousand words but how to organize thousands of pictures? |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. |
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MILAB; no proj |
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Admin @ si @ LRD2018 |
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3111 |
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Y. Patel; Lluis Gomez; Raul Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. |
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DAG; 600.084; 601.338; 600.121 |
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Admin @ si @ PGG2018 |
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3177 |
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Alejandro Cartas; Estefania Talavera; Petia Radeva; Mariella Dimiccoli |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users. |
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MILAB; no proj |
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no |
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Admin @ si @ CTR2018 |
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3184 |
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Author |
Md. Mostafa Kamal Sarker; Mohammed Jabreel; Hatem A. Rashwan; Syeda Furruka Banu; Antonio Moreno; Petia Radeva; Domenec Puig |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network. |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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MILAB; no proj |
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no |
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Admin @ si @ KJR2018 |
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3235 |
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Author |
Hugo Prol; Vincent Dumoulin; Luis Herranz |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Cross-Modulation Networks for Few-Shot Learning |
Type ![sorted by Type field, descending order (down)](img/sort_desc.gif) |
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2018 |
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Arxiv |
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A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art. |
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LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ PDH2018 |
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3248 |
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