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Damian Sojka; Yuyang Liu; Dipam Goswami; Sebastian Cygert; Bartłomiej Twardowski; Joost van de Weijer |
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Technical Report for ICCV 2023 Visual Continual Learning Challenge: Continuous Test-time Adaptation for Semantic Segmentation |
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2023 |
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Arxiv |
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The goal of the challenge is to develop a test-time adaptation (TTA) method, which could adapt the model to gradually changing domains in video sequences for semantic segmentation task. It is based on a synthetic driving video dataset – SHIFT. The source model is trained on images taken during daytime in clear weather. Domain changes at test-time are mainly caused by varying weather conditions and times of day. The TTA methods are evaluated in each image sequence (video) separately, meaning the model is reset to the source model state before the next sequence. Images come one by one and a prediction has to be made at the arrival of each frame. Each sequence is composed of 401 images and starts with the source domain, then gradually drifts to a different one (changing weather or time of day) until the middle of the sequence. In the second half of the sequence, the domain gradually shifts back to the source one. Ground truth data is available only for the validation split of the SHIFT dataset, in which there are only six sequences that start and end with the source domain. We conduct an analysis specifically on those sequences. Ground truth data for test split, on which the developed TTA methods are evaluated for leader board ranking, are not publicly available.
The proposed solution secured a 3rd place in a challenge and received an innovation award. Contrary to the solutions that scored better, we did not use any external pretrained models or specialized data augmentations, to keep the solutions as general as possible. We have focused on analyzing the distributional shift and developing a method that could adapt to changing data dynamics and generalize across different scenarios. |
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LAMP |
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Admin @ si @ SLG2023 |
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3993 |
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Mingyi Yang; Fei Yang; Luka Murn; Marc Gorriz Blanch; Juil Sock; Shuai Wan; Fuzheng Yang; Luis Herranz |
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Title |
Task-Switchable Pre-Processor for Image Compression for Multiple Machine Vision Tasks |
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2024 |
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IEEE Transactions on Circuits and Systems for Video Technology |
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M Yang, F Yang, L Murn, MG Blanch, J Sock, S Wan, F Yang, L Herranz |
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Visual content is increasingly being processed by machines for various automated content analysis tasks instead of being consumed by humans. Despite the existence of several compression methods tailored for machine tasks, few consider real-world scenarios with multiple tasks. In this paper, we aim to address this gap by proposing a task-switchable pre-processor that optimizes input images specifically for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. The proposed task-switchable pre-processor adeptly maintains relevant semantic information based on the specific characteristics of different downstream tasks, while effectively suppressing irrelevant information to reduce bitrate. To enhance the processing of semantic information for diverse tasks, we leverage pre-extracted semantic features to modulate the pixel-to-pixel mapping within the pre-processor. By switching between different modulations, multiple tasks can be seamlessly incorporated into the system. Extensive experiments demonstrate the practicality and simplicity of our approach. It significantly reduces the number of parameters required for handling multiple tasks while still delivering impressive performance. Our method showcases the potential to achieve efficient and effective compression for machine vision tasks, supporting the evolving demands of real-world applications. |
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Admin @ si @ YYM2024 |
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4007 |
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Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente |
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Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy |
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2014 |
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Journal of Biomedical Optics |
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JBiO |
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19 |
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12 |
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126004-1 - 126004-9 |
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The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. |
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ADAS; 600.076 |
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Admin @ si @ BLS2014 |
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2563 |
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N. Pares; J.R. Serra |
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Title |
Tailleur: El problema del sastre. |
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1992 |
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V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Admin @ si @ PaS1992 |
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252 |
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Amir A.Amini; Yasheng Chen; Mohamed Elayyadi; Petia Radeva |
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Tag Surface Reconstruction and Tracking of Myocardial Beads from SPAMM-MRI with Parametric B-Spline Surfaces |
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2001 |
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IEEE Transactions on Medical Imaging |
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TMI |
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20 |
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2 |
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94–103 |
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B-spline surfaces, cardiac motion, myocardial beads, myocardial infarction, tagged MRI. |
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Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization, and create tag planes intersecting image slices. The resulting grid of signal voids allows for tracking deformations of tissues in otherwise homogeneous-signal myocardial regions. In this paper, we propose a specific spatial modulation of magnetization (SPAMM) imaging protocol together with efficient techniques for measurement of three-dimensional (3-D) motion of material points of the human heart (referred to as myocardial beads) from images collected with the SPAMM method. The techniques make use of tagged images in orthogonal views by explicitly reconstructing 3-D B-spline surface representation of tag planes (tag planes in two orthogonal orientations intersecting the short-axis (SA) image slices and tag planes in an orientation orthogonal to the short-axis tag planes intersecting long-axis (LA) image slices). The developed methods allow for viewing deformations of 3-D tag surfaces, spatial correspondence of long-axis and short-axis image slice and tag positions, as well as nonrigid movement of myocardial beads as a function of time. |
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MILAB |
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BCNPCL @ bcnpcl @ ACE2001; IAM @ iam @ ACE2001 |
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180 |
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Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste |
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TADAM: Task dependent adaptive metric for improved few-shot learning |
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2018 |
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32nd Annual Conference on Neural Information Processing Systems |
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Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. |
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Montreal; Canada; December 2018 |
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NIPS |
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ISE; 600.098; 600.119 |
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Admin @ si @ ORL2018 |
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3140 |
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Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados |
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Title |
Table Detection in Invoice Documents by Graph Neural Networks |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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122-127 |
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Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.140; 601.302; 602.167; 600.121; 600.141 |
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Admin @ si @ RDG2019 |
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3355 |
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Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados |
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Table detection in business document images by message passing networks |
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2022 |
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Pattern Recognition |
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PR |
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127 |
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108641 |
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Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. |
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July 2022 |
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Elsevier |
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DAG; 600.162; 600.121 |
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Admin @ si @ RGR2022 |
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3729 |
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Albert Ali Salah; E. Pauwels; R. Tavenard; Theo Gevers |
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T-Patterns Revisited: Mining for Temporal Patterns in Sensor Data |
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2010 |
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Sensors |
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SENS |
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10 |
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8 |
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7496-7513 |
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sensor networks; temporal pattern extraction; T-patterns; Lempel-Ziv; Gaussian mixture model; MERL motion data |
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The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events. |
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ALTRES;ISE |
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1845 |
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David Lloret; Derek L.G. Hill |
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System for live fusion of 2-D ultrasound scans to pre-interventional MR volumes of a patient. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes, 2:23–24. |
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David Lloret; Joan Serrat |
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System for calibration of a stereotatic frame. |
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1999 |
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Proceeding of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS @ adas @ LlS1999 |
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Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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System and method for video classification using a hybrid unsupervised and supervised multi-layer architecture |
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2018 |
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US9946933B2 |
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A computer-implemented video classification method and system are disclosed. The method includes receiving an input video including a sequence of frames. At least one transformation of the input video is generated, each transformation including a sequence of frames. For the input video and each transformation, local descriptors are extracted from the respective sequence of frames. The local descriptors of the input video and each transformation are aggregated to form an aggregated feature vector with a first set of processing layers learned using unsupervised learning. An output classification value is generated for the input video, based on the aggregated feature vector with a second set of processing layers learned using supervised learning. |
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ADAS; 600.118 |
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Admin @ si @ SGV2018 |
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3255 |
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Michal Drozdzal; Petia Radeva; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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System and Method for Improving a Discriminative Model |
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2012 |
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US 61/450,886 |
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Given Imaging |
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US Patent Office |
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Notes |
MILAB; OR;MV |
Approved |
no |
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Call Number |
Admin @ si @ DRS2012a |
Serial |
1896 |
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| Permanent link to this record |
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Author |
Michal Drozdzal; Santiago Segui; Petia Radeva; Jordi Vitria; Laura Igual |
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Title |
System and Method for Displaying Motility Events in an in Vivo Image Stream |
Type |
Patent |
| |
Year |
2011 |
Publication |
US 61/592,786 |
Abbreviated Journal |
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Volume |
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Pages |
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Keywords |
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Abstract |
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Address |
Given Imaging |
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Corporate Author |
US Patent Office |
Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Title |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
MILAB; OR;MV |
Approved |
no |
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| |
Call Number |
Admin @ si @ DSR2011 |
Serial |
1897 |
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| Permanent link to this record |
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Author |
Michal Drozdzal; Petia Radeva; Santiago Segui; Laura Igual; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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| |
Title |
System and method for automatic detection of in vivo contraction video sequences |
Type |
Patent |
| |
Year |
2012 |
Publication |
US20120057766 |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Keywords |
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Abstract |
Publication date: 2012/3/8 |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
MILAB; OR;MV |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DRS2012b |
Serial |
2071 |
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| Permanent link to this record |
