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Author |
Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski |
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Title |
Bayesian Flow Networks in Continual Learning |
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2023 |
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arxiv |
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Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data. |
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LAMP |
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Admin @ si @ PDT2023 |
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3972 |
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Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz |
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Title |
Unsupervised Domain Adaptation without Source Data by Casting a BAIT |
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2020 |
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arXiv:2010.12427
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods. |
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LAMP; 600.120 |
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Admin @ si @ YWW2020 |
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3539 |
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Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer |
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Title |
Simple and effective localized attribute representations for zero-shot learning |
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2020 |
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arXiv:2006.05938
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps. |
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LAMP; 600.120 |
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Admin @ si @ YWH2020 |
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3542 |
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Author |
Mikel Menta; Adriana Romero; Joost Van de Weijer |
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Title |
Learning to adapt class-specific features across domains for semantic segmentation |
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2020 |
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Arxiv |
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arXiv:2001.08311
Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain. The great majority of existing domain adaptation models rely on image translation networks, which often contain a huge amount of domain-specific parameters. Additionally, the feature adaptation step often happens globally, at a coarse level, hindering its applicability to tasks such as semantic segmentation, where details are of crucial importance to provide sharp results. In this thesis, we present a novel architecture, which learns to adapt features across domains by taking into account per class information. To that aim, we design a conditional pixel-wise discriminator network, whose output is conditioned on the segmentation masks. Moreover, following recent advances in image translation, we adopt the recently introduced StarGAN architecture as image translation backbone, since it is able to perform translations across multiple domains by means of a single generator network. Preliminary results on a segmentation task designed to assess the effectiveness of the proposed approach highlight the potential of the model, improving upon strong baselines and alternative designs. |
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LAMP; 600.120 |
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Admin @ si @ MRW2020 |
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3545 |
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Author |
Guillem Cucurull; Pau Rodriguez; Vacit Oguz Yazici; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez |
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Title |
Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks |
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2018 |
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Arxiv |
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arXiv:1802.06757
Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. To sense the whys of certain social user’s demands and cultural-driven interests, however, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited since this process has been typically been text-based. Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So the key contribution here is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between those posted images and their accompanying texts, which can be successfully modeled using deep neural networks for personality estimation. The experimental results are consistent with previous cyber-psychology results based on texts or images.
In addition, classification results on some traits show that some patterns emerge in the set of images corresponding to a specific text, in essence to those representing an abstract concept. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts. |
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ISE; 600.098; 600.119 |
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Admin @ si @ CRY2018 |
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3550 |
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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 |
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End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks |
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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 |
Senmao Li; Joost van de Weijer; Taihang Hu; Fahad Shahbaz Khan; Qibin Hou; Yaxing Wang; Jian Yang |
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Title |
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing |
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Miscellaneous |
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2023 |
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Arxiv |
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A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images. They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions, and unexpected changes in nonselected regions. (2) They require careful text prompt editing where the prompt should include all visual objects in the input image. To address this, we propose two improvements: (1) Only optimizing the input of the value linear network in the cross-attention layers, is sufficiently powerful to reconstruct a real image. (2) We propose attention regularization to preserve the object-like attention maps after editing, enabling us to obtain accurate style editing without invoking significant structural changes. We further improve the editing technique which is used for the unconditional branch of classifier-free guidance, as well as the conditional one as used by P2P. Extensive experimental prompt-editing results on a variety of images, demonstrate qualitatively and quantitatively that our method has superior editing capabilities than existing and concurrent works. |
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LAMP |
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no |
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Admin @ si @ LWH2023 |
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3870 |
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Author |
Hugo Prol; Vincent Dumoulin; Luis Herranz |
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Title |
Cross-Modulation Networks for Few-Shot Learning |
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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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Admin @ si @ PDH2018 |
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3248 |
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Author |
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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Admin @ si @ SBS2023 |
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3863 |
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Josep Llados; J. Lopez-Krahe; Enric Marti |
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A Hough-based method for hatched pattern detection in maps and diagrams. |
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1999 |
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Proceedings of the International Conference on Document Analysis and Recognition. |
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Bangalore-India |
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DAG @ dag @ LlM1999b |
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Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Multiresolution colour texture representations for tile classification |
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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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ADAS @ adas @ LBV1999a |
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Daniel Ponsa; A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; Jordi Vitria |
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Regularized EM |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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invisible;ADAS;OR;MILAB;MV |
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ADAS @ adas @ PSL1999 |
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David Guillamet; Jordi Vitria |
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Using Eigenspace analysis of color distributions for object recognition |
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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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OR;MV |
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BCNPCL @ bcnpcl @ GuV1999a |
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A. Pujol; Felipe Lumbreras; Javier Varona; Juan J. Villanueva |
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Template matching through invariant eigenspace projection. |
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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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ADAS @ adas @ PLV1999 |
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6 |
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| Permanent link to this record |
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Author |
Josep Llados; Felipe Lumbreras; Javier Varona |
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Title |
A multidocument platform for automatic reading of identity cards. |
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Miscellaneous |
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Year |
1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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ADAS;DAG |
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Call Number |
ADAS @ adas @ LLV1999 |
Serial |
7 |
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| Permanent link to this record |
