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Author Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados
Title Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild Type Miscellaneous
Year 2021 Publication Arxiv Abbreviated Journal
Volume Issue (down) Pages
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Abstract 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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Notes DAG; 600.121 Approved no
Call Number Admin @ si @ RDB2021 Serial 3674
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Author Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados
Title A Self-supervised Inverse Graphics Approach for Sketch Parametrization Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12916 Issue (down) Pages 28-42
Keywords
Abstract The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.
Address Lausanne; Suissa; September 2021
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
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Area Expedition Conference ICDAR
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ SRR2021 Serial 3675
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Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title Graph-Based Deep Generative Modelling for Document Layout Generation Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12917 Issue (down) Pages 525-537
Keywords
Abstract One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
Address Lausanne; Suissa; September 2021
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
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Area Expedition Conference
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021 Serial 3676
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Author Josep Llados
Title The 5G of Document Intelligence Type Conference Article
Year 2021 Publication 3rd Workshop on Future of Document Analysis and Recognition Abbreviated Journal
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Address Lausanne; Suissa; September 2021
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Area Expedition Conference FDAR
Notes DAG Approved no
Call Number Admin @ si @ Serial 3677
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Author Shun Yao; Fei Yang; Yongmei Cheng; Mikhail Mozerov
Title 3D Shapes Local Geometry Codes Learning with SDF Type Conference Article
Year 2021 Publication International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue (down) Pages 2110-2117
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Abstract A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF [17] that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics.
Address VIRTUAL; October 2021
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Area Expedition Conference ICCVW
Notes LAMP Approved no
Call Number Admin @ si @ YYC2021 Serial 3681
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Author Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui
Title Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation Type Conference Article
Year 2021 Publication Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) Abbreviated Journal
Volume Issue (down) Pages
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Abstract Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors.
Address Online; December 7-10, 2021
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Area Expedition Conference NIPS
Notes LAMP; 600.147; 600.141 Approved no
Call Number Admin @ si @ Serial 3691
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Author Jose Elias Yauri; Aura Hernandez-Sabate; Pau Folch; Debora Gil
Title Mental Workload Detection Based on EEG Analysis Type Conference Article
Year 2021 Publication Artificial Intelligent Research and Development. Proceedings 23rd International Conference of the Catalan Association for Artificial Intelligence. Abbreviated Journal
Volume 339 Issue (down) Pages 268-277
Keywords Cognitive states; Mental workload; EEG analysis; Neural Networks.
Abstract The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement.
Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training.
In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.
Address Virtual; October 20-22 2021
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Area Expedition Conference CCIA
Notes IAM; 600.139; 600.118; 600.145 Approved no
Call Number Admin @ si @ Serial 3723
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Author Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown
Title Noise Prism: A Novel Multispectral Visualization Technique Type Journal Article
Year 2021 Publication 31st Color and Imaging Conference Abbreviated Journal
Volume Issue (down) Pages
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Abstract A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique.
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Area Expedition Conference CIC
Notes MACO; CIC Approved no
Call Number Admin @ si @ CVL2021 Serial 4000
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