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David Guillamet; Jordi Vitria |
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Local Discriminant Regions Using Support Vector Machines for Object Recognition. |
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2000 |
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Advances in Pattern Recognition, Lecture Notes in Computer Science 1876: 550–559, Springer Verlag. |
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OR;MV |
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BCNPCL @ bcnpcl @ GuV2000 a |
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240 |
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Oriol Ramos Terrades; Ernest Valveny |
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Local Norm Features based on ridgelets Transform |
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2005 |
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8th International Conference on Document Analysis and Recognition (ICDAR´05), 700–704 |
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DAG |
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DAG @ dag @ RaV2005d |
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642 |
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Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer |
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Title |
Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method |
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2022 |
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Arxiv |
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We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in this https URL. |
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LAMP; 600.147 |
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Admin @ si @ YWW2022b |
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3815 |
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Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados |
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Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild |
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2021 |
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Arxiv |
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This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild. |
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DAG; 600.121 |
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Admin @ si @ RDB2021 |
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3674 |
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Oriol Pujol; Petia Radeva |
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Title |
Lumen Detection in Ivus Image Using Snakes in a Statical Framework. |
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2002 |
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XX Congreso Anual de la Sociedad Española de Ingenieria Biomedica CASEIB 2002, 1: 129–132. |
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Saragossa, Espanya |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ PuR2002 |
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315 |
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Bonifaz Stuhr; Jurgen Brauer; Bernhard Schick; Jordi Gonzalez |
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Title |
Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation |
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2023 |
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Arxiv |
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A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level. |
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ISE |
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Admin @ si @ SBS2023 |
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3863 |
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Author |
Carme Julia |
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Title |
Missig Data Matrix Factorization Addressing the Structure from Motion Problem |
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2008 |
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CVC–UAB |
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Bellaterra |
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978–84–935251–6–3 |
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Admin @ si @ Jul2008 |
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1104 |
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Hao Wu; Alejandro Ariza-Casabona; Bartłomiej Twardowski; Tri Kurniawan Wijaya |
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MM-GEF: Multi-modal representation meet collaborative filtering |
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2023 |
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ARXIV |
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In modern e-commerce, item content features in various modalities offer accurate yet comprehensive information to recommender systems. The majority of previous work either focuses on learning effective item representation during modelling user-item interactions, or exploring item-item relationships by analysing multi-modal features. Those methods, however, fail to incorporate the collaborative item-user-item relationships into the multi-modal feature-based item structure. In this work, we propose a graph-based item structure enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion, which effectively combines the latent item structure underlying multi-modal contents with the collaborative signals. Instead of processing the content feature in different modalities separately, we show that the early-fusion of multi-modal features provides significant improvement. MM-GEF learns refined item representations by injecting structural information obtained from both multi-modal and collaborative signals. Through extensive experiments on four publicly available datasets, we demonstrate systematical improvements of our method over state-of-the-art multi-modal recommendation methods. |
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LAMP |
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Admin @ si @ WAT2023 |
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3988 |
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Daniel Ponsa; Jordi Vitria |
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Mobile monitoring system using an agent-oriented approach |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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Bilbao |
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ADAS;OR;MV |
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ADAS @ adas @ DaV1999 |
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21 |
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Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Syeda Furruka Banu; Farhan Akram; Forhad U. H. Chowdhury; Kabir Ahmed Choudhury; Sylvie Chambon; Petia Radeva; Domenec Puig |
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MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network |
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2019 |
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Arxiv |
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CoRR abs/1907.00856
Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%. |
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MILAB; no menciona |
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Admin @ si @ MRA2019 |
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3384 |
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Jose Manuel Alvarez; Antonio Lopez |
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Model-based road detection using shadowless features and on-line learning |
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2009 |
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BMVA one–day technical meeting on vision for automotive applications |
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road detection |
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London, UK |
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1272 |
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Cristina Cañero; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Modelling the Acquisition Geometry of a C-arm Angiography System for 3D Reconstruction. |
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2002 |
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BCNPCL @ bcnpcl @ CFM2002b |
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Hannes Mueller; Andre Groger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
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Monitoring War Destruction from Space: A Machine Learning Approach |
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2020 |
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Arxiv |
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Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques combined with data augmentation to expand training samples. We apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. The approach allows generating destruction data with unprecedented scope, resolution, and frequency – only limited by the available satellite imagery – which can alleviate data limitations decisively. |
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Admin @ si @ MGH2020 |
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3489 |
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Carme Julia; Joan Serrat; Antonio Lopez; Felipe Lumbreras; Daniel Ponsa |
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Motion segmentation through factorization. Application to night driving assistance |
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2006 |
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International Conference on Computer Vision Theory and Applications, (2) |
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ADAS @ adas @ JSL2006a |
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638 |
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David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Motion-induced error correction in ultrasound imaging. |
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2002 |
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1st. International Symposium on 3D Data Processing Visualization and Transmission 3DPTV. |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ LSL2002 |
Serial |
295 |
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