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	Author	Fei Yang; Kai Wang; Joost Van de Weijer
	Title	ScrollNet: DynamicWeight Importance for Continual Learning			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops	Abbreviated Journal
	Volume		Issue		Pages	3345-3355
	Keywords
	Abstract	The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plasticity in other parameters. The importance of weights for each task can be determined either explicitly through learning a task-specific mask during training (e.g., parameter isolation-based approaches) or implicitly by introducing a regularization term (e.g., regularization-based approaches). However, all these methods assume that the importance of weights for each task is unknown prior to data exposure. In this paper, we propose ScrollNet as a scrolling neural network for continual learning. ScrollNet can be seen as a dynamic network that assigns the ranking of weight importance for each task before data exposure, thus achieving a more favorable stability-plasticity tradeoff during sequential task learning by reassigning this ranking for different tasks. Additionally, we demonstrate that ScrollNet can be combined with various CL methods, including regularization-based and replay-based approaches. Experimental results on CIFAR100 and TinyImagenet datasets show the effectiveness of our proposed method.
	Address	Paris; France; October 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCVW
	Notes	LAMP			Approved	no
	Call Number	Admin @ si @ WWW2023			Serial	3945
Permanent link to this record



	Author	Anthony Cioppa; Silvio Giancola; Vladimir Somers; Floriane Magera; Xin Zhou; Hassan Mkhallati; Adrien Deliège; Jan Held; Carlos Hinojosa; Amir M. Mansourian; Pierre Miralles; Olivier Barnich; Christophe De Vleeschouwer; Alexandre Alahi; Bernard Ghanem; Marc Van Droogenbroeck; Abdullah Kamal; Adrien Maglo; Albert Clapes; Amr Abdelaziz; Artur Xarles; Astrid Orcesi; Atom Scott; Bin Liu; Byoungkwon Lim; Chen Chen; Fabian Deuser; Feng Yan; Fufu Yu; Gal Shitrit; Guanshuo Wang; Gyusik Choi; Hankyul Kim; Hao Guo; Hasby Fahrudin; Hidenari Koguchi; Håkan Ardo; Ibrahim Salah; Ido Yerushalmy; Iftikar Muhammad; Ikuma Uchida; Ishay Beery; Jaonary Rabarisoa; Jeongae Lee; Jiajun Fu; Jianqin Yin; Jinghang Xu; Jongho Nang; Julien Denize; Junjie Li; Junpei Zhang; Juntae Kim; Kamil Synowiec; Kenji Kobayashi; Kexin Zhang; Konrad Habel; Kota Nakajima; Licheng Jiao; Lin Ma; Lizhi Wang; Luping Wang; Menglong Li; Mengying Zhou; Mohamed Nasr; Mohamed Abdelwahed; Mykola Liashuha; Nikolay Falaleev; Norbert Oswald; Qiong Jia; Quoc-Cuong Pham; Ran Song; Romain Herault; Rui Peng; Ruilong Chen; Ruixuan Liu; Ruslan Baikulov; Ryuto Fukushima; Sergio Escalera; Seungcheon Lee; Shimin Chen; Shouhong Ding; Taiga Someya; Thomas B. Moeslund; Tianjiao Li; Wei Shen; Wei Zhang; Wei Li; Wei Dai; Weixin Luo; Wending Zhao; Wenjie Zhang; Xinquan Yang; Yanbiao Ma; Yeeun Joo; Yingsen Zeng; Yiyang Gan; Yongqiang Zhu; Yujie Zhong; Zheng Ruan; Zhiheng Li; Zhijian Huang; Ziyu Meng
	Title	SoccerNet 2023 Challenges Results			Type	Miscellaneous
	Year	2023	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on this https URL. Baselines and development kits can be found on this https URL.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HUPBA			Approved	no
	Call Number	Admin @ si @ CGS2023			Serial	3991
Permanent link to this record



	Author	Matej Kristan; Jiri Matas; Martin Danelljan; Michael Felsberg; Hyung Jin Chang; Luka Cehovin Zajc; Alan Lukezic; Ondrej Drbohlav; Zhongqun Zhang; Khanh-Tung Tran; Xuan-Son Vu; Johanna Bjorklund; Christoph Mayer; Yushan Zhang; Lei Ke; Jie Zhao; Gustavo Fernandez; Noor Al-Shakarji; Dong An; Michael Arens; Stefan Becker; Goutam Bhat; Sebastian Bullinger; Antoni B. Chan; Shijie Chang; Hanyuan Chen; Xin Chen; Yan Chen; Zhenyu Chen; Yangming Cheng; Yutao Cui; Chunyuan Deng; Jiahua Dong; Matteo Dunnhofer; Wei Feng; Jianlong Fu; Jie Gao; Ruize Han; Zeqi Hao; Jun-Yan He; Keji He; Zhenyu He; Xiantao Hu; Kaer Huang; Yuqing Huang; Yi Jiang; Ben Kang; Jin-Peng Lan; Hyungjun Lee; Chenyang Li; Jiahao Li; Ning Li; Wangkai Li; Xiaodi Li; Xin Li; Pengyu Liu; Yue Liu; Huchuan Lu; Bin Luo; Ping Luo; Yinchao Ma; Deshui Miao; Christian Micheloni; Kannappan Palaniappan; Hancheol Park; Matthieu Paul; HouWen Peng; Zekun Qian; Gani Rahmon; Norbert Scherer-Negenborn; Pengcheng Shao; Wooksu Shin; Elham Soltani Kazemi; Tianhui Song; Rainer Stiefelhagen; Rui Sun; Chuanming Tang; Zhangyong Tang; Imad Eddine Toubal; Jack Valmadre; Joost van de Weijer; Luc Van Gool; Jash Vira; Stephane Vujasinovic; Cheng Wan; Jia Wan; Dong Wang; Fei Wang; Feifan Wang; He Wang; Limin Wang; Song Wang; Yaowei Wang; Zhepeng Wang; Gangshan Wu; Jiannan Wu; Qiangqiang Wu; Xiaojun Wu; Anqi Xiao; Jinxia Xie; Chenlong Xu; Min Xu; Tianyang Xu; Yuanyou Xu; Bin Yan; Dawei Yang; Ming-Hsuan Yang; Tianyu Yang; Yi Yang; Zongxin Yang; Xuanwu Yin; Fisher Yu; Hongyuan Yu; Qianjin Yu; Weichen Yu; YongSheng Yuan; Zehuan Yuan; Jianlin Zhang; Lu Zhang; Tianzhu Zhang; Guodongfang Zhao; Shaochuan Zhao; Yaozong Zheng; Bineng Zhong; Jiawen Zhu; Xuefeng Zhu; Yueting Zhuang; ChengAo Zong; Kunlong Zuo
	Title	The First Visual Object Tracking Segmentation VOTS2023 Challenge Results			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops	Abbreviated Journal
	Volume		Issue		Pages	1796-1818
	Keywords
	Abstract	The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website\footnote https://www.votchallenge.net/vots2023/.
	Address	Paris; France; October 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICCVW
	Notes	LAMP			Approved	no
	Call Number	Admin @ si @ KMD2023			Serial	3939
Permanent link to this record



	Author	Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa
	Title	Corn kernel classification from few training samples			Type	Journal
	Year	2023	Publication	Artificial Intelligence in Agriculture	Abbreviated Journal
	Volume	9	Issue		Pages	89-99
	Keywords
	Abstract	This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SVC2023			Serial	3892
Permanent link to this record



	Author	Stepan Simsa; Milan Sulc; Michal Uricar; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas
	Title	DocILE Benchmark for Document Information Localization and Extraction			Type	Conference Article
	Year	2023	Publication	17th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	14188	Issue		Pages	147–166
	Keywords	Document AI; Information Extraction; Line Item Recognition; Business Documents; Intelligent Document Processing
	Abstract	This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly 1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: (i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin; (ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table; (iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set. The benchmark comes with several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at https://github.com/rossumai/docile.
	Address	San Jose; CA; USA; August 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ SSU2023			Serial	3903
Permanent link to this record



	Author	Mohamed Ali Souibgui; Pau Torras; Jialuo Chen; Alicia Fornes
	Title	An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts			Type	Conference Article
	Year	2023	Publication	7th International Workshop on Historical Document Imaging and Processing	Abbreviated Journal
	Volume		Issue		Pages	7-12
	Keywords
	Abstract	This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	HIP
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ STC2023			Serial	3849
Permanent link to this record



	Author	Francesc Net; Marc Folia; Pep Casals; Lluis Gomez
	Title	Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections			Type	Conference Article
	Year	2023	Publication	17th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	14191	Issue		Pages	3-17
	Keywords	Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning
	Abstract	This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset.
	Address	San Jose; CA; USA; August 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ NFC2023			Serial	3859
Permanent link to this record



	Author	Patricia Suarez; Dario Carpio; Angel Sappa
	Title	A Deep Learning Based Approach for Synthesizing Realistic Depth Maps			Type	Conference Article
	Year	2023	Publication	22nd International Conference on Image Analysis and Processing	Abbreviated Journal
	Volume	14234	Issue		Pages	369–380
	Keywords
	Abstract	This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality.
	Address	Udine; Italia; Setember 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICIAP
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SCS2023a			Serial	3968
Permanent link to this record



	Author	Patricia Suarez; Dario Carpio; Angel Sappa
	Title	Depth Map Estimation from a Single 2D Image			Type	Conference Article
	Year	2023	Publication	17th International Conference on Signal-Image Technology & Internet-Based Systems	Abbreviated Journal
	Volume		Issue		Pages	347-353
	Keywords
	Abstract	This paper presents an innovative architecture based on a Cycle Generative Adversarial Network (CycleGAN) for the synthesis of high-quality depth maps from monocular images. The proposed architecture leverages a diverse set of loss functions, including cycle consistency, contrastive, identity, and least square losses, to facilitate the generation of depth maps that exhibit realism and high fidelity. A notable feature of the approach is its ability to synthesize depth maps from grayscale images without the need for paired training data. Extensive comparisons with different state-of-the-art methods show the superiority of the proposed approach in both quantitative metrics and visual quality. This work addresses the challenge of depth map synthesis and offers significant advancements in the field.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	SITIS
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SCS2023b			Serial	4009
Permanent link to this record



	Author	Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich
	Title	Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	412-421
	Keywords
	Abstract	This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement.
	Address	Vancouver; Canada; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ LNS2023b			Serial	3915
Permanent link to this record



	Author	Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Chenyang Wang; Junjun Jiang; Xianming Liu; Zhiwei Zhong; Dai Bin; Li Ruodi; Li Shengye
	Title	Thermal Image Super-Resolution Challenge Results-PBVS 2023			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	470-478
	Keywords
	Abstract	This paper presents the results of two tracks from the fourth Thermal Image Super-Resolution (TISR) challenge, held at the Perception Beyond the Visible Spectrum (PBVS) 2023 workshop. Track-1 uses the same thermal image dataset as previous challenges, with 951 training images and 50 validation images at each resolution. In this track, two evaluations were conducted: the first consists of generating a SR image from a HR thermal noisy image downsampled by four, and the second consists of generating a SR image from a mid-resolution image and compare it with its semi-registered HR image (acquired with another camera). The results of Track-1 outperformed those from last year’s challenge. On the other hand, Track-2 uses a new acquired dataset consisting of 160 registered visible and thermal images of the same scenario for training and 30 validation images. This year, more than 150 teams participated in the challenge tracks, demonstrating the community’s ongoing interest in this topic.
	Address	Vancouver; Canada; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ RSV2023			Serial	3914
Permanent link to this record



	Author	Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie
	Title	Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan			Type	Journal Article
	Year	2023	Publication	Scientific Reports	Abbreviated Journal	ScR
	Volume	13	Issue		Pages	11257
	Keywords
	Abstract	This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ BOL2023			Serial	3976
Permanent link to this record



	Author	Patricia Suarez; Angel Sappa
	Title	Toward a Thermal Image-Like Representation			Type	Conference Article
	Year	2023	Publication	Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	133-140
	Keywords
	Abstract	This paper proposes a novel model to obtain thermal image-like representations to be used as an input in any thermal image compressive sensing approach (e.g., thermal image: filtering, enhancing, super-resolution). Thermal images offer interesting information about the objects in the scene, in addition to their temperature. Unfortunately, in most of the cases thermal cameras acquire low resolution/quality images. Hence, in order to improve these images, there are several state-of-the-art approaches that exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). In these SOTA approaches visible images are fused at different levels without paying attention the images acquire information at different bands of the spectral. In this paper a novel approach is proposed to generate thermal image-like representations from a low cost visible images, by means of a contrastive cycled GAN network. Obtained representations (synthetic thermal image) can be later on used to improve the low quality thermal image of the same scene. Experimental results on different datasets are presented.
	Address	Lisboa; Portugal; February 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	VISIGRAPP
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SuS2023b			Serial	3927
Permanent link to this record



	Author	Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas
	Title	Overview of DocILE 2023: Document Information Localization and Extraction			Type	Conference Article
	Year	2023	Publication	International Conference of the Cross-Language Evaluation Forum for European Languages	Abbreviated Journal
	Volume	14163	Issue		Pages	276–293
	Keywords	Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding
	Abstract	This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets.
	Address	Thessaloniki; Greece; September 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CLEF
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ SUS2023a			Serial	3924
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	Author	Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang
	Title	NTIRE 2023 challenge on image denoising: Methods and results			Type	Conference Article
	Year	2023	Publication	Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	1904-1920
	Keywords
	Abstract	This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising.
	Address	Vancouver; Canada; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MACO; CIC			Approved	no
	Call Number	Admin @ si @ LZT2023			Serial	3910
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