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| Author | Danna Xue; Fei Yang; Pei Wang; Luis Herranz; Jinqiu Sun; Yu Zhu; Yanning Zhang | ||||
| Title | SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision | Type | Conference Article | ||
| Year | 2022 | Publication | 30th ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue  |
Pages | 6539-6548 | ||
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| Abstract | Accurate semantic segmentation models typically require significant computational resources, inhibiting their use in practical applications. Recent works rely on well-crafted lightweight models to achieve fast inference. However, these models cannot flexibly adapt to varying accuracy and efficiency requirements. In this paper, we propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference depending on the desired accuracy-efficiency tradeoff. More specifically, we employ parametrized channel slimming by stepwise downward knowledge distillation during training. Motivated by the observation that the differences between segmentation results of each submodel are mainly near the semantic borders, we introduce an additional boundary guided semantic segmentation loss to further improve the performance of each submodel. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance than independent models. Extensive experiments on semantic segmentation benchmarks, Cityscapes and CamVid, demonstrate the generalization ability of our framework. | ||||
| Address | Lisboa, Portugal, October 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Association for Computing Machinery | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-9203-7 | Medium | ||
| Area | Expedition | Conference | MM | ||
| Notes | MACO; 600.161; 601.400 | Approved | no | ||
| Call Number | Admin @ si @ XYW2022 | Serial | 3758 | ||
| Permanent link to this record | |||||
| Author | David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina | ||||
| Title | A Survey of FPGA-Based Vision Systems for Autonomous Cars | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACESS |
| Volume | 10 | Issue |
Pages | 132525-132563 | |
| Keywords | Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures | ||||
| Abstract | On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. | ||||
| Address | 16 December 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE | 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 | IAM; 600.166 | Approved | no | ||
| Call Number | Admin @ si @ CNB2022 | Serial | 3760 | ||
| Permanent link to this record | |||||
| Author | Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva | ||||
| Title | Bayesian deep learning for semantic segmentation of food images | Type | Journal Article | ||
| Year | 2022 | Publication | Computers and Electrical Engineering | Abbreviated Journal | CEE |
| Volume | 103 | Issue |
Pages | 108380 | |
| Keywords | Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis | ||||
| Abstract | Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. | ||||
| Address | October 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Science Direct | 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 | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANR2022 | Serial | 3763 | ||
| Permanent link to this record | |||||
| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 36th Conference on Neural Information Processing Systems | Abbreviated Journal | |
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Pages | |||
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| Abstract | 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. |
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| Address | Virtual; November 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | NEURIPS | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ YWW2022a | Serial | 3792 | ||
| Permanent link to this record | |||||
| Author | Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang | ||||
| Title | DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video | Type | Conference Article | ||
| Year | 2022 | Publication | 47th International Conference on Acoustics, Speech, and Signal Processing | Abbreviated Journal | |
| Volume | Issue |
Pages | |||
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| Abstract | In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. | ||||
| Address | Virtual; May 2022 | ||||
| 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 | ICASSP | ||
| Notes | MACO; 600.161; 601.379 | Approved | no | ||
| Call Number | Admin @ si @ ZHM2022a | Serial | 3765 | ||
| Permanent link to this record | |||||
| Author | German Barquero; Johnny Nuñez; Sergio Escalera; Zhen Xu; Wei-Wei Tu; Isabelle Guyon | ||||
| Title | Didn’t see that coming: a survey on non-verbal social human behavior forecasting | Type | Conference Article | ||
| Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
| Volume | 173 | Issue |
Pages | 139-178 | |
| Keywords | |||||
| Abstract | Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises
methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarized and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues. |
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| Address | Virtual; June 2022 | ||||
| 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 | PMLR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ BNE2022 | Serial | 3766 | ||
| Permanent link to this record | |||||
| Author | Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra | ||||
| Title | Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation | Type | Conference Article | ||
| Year | 2022 | Publication | 47th International Conference on Acoustics, Speech, and Signal Processing | Abbreviated Journal | |
| Volume | Issue |
Pages | |||
| Keywords | Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding | ||||
| Abstract | In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. | ||||
| Address | Singapore; May 2022 | ||||
| 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 | ICASSP | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ MAD2022 | Serial | 3767 | ||
| 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 2022 | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue |
Pages | 350-358 | ||
| Keywords | |||||
| Abstract | This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. | ||||
| Address | New Orleans; USA; June 2022 | ||||
| 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 @ LNS2022 | Serial | 3768 | ||
| Permanent link to this record | |||||
| Author | Adam Fodor; Rachid R. Saboundji; Julio C. S. Jacques Junior; Sergio Escalera; David Gallardo Pujol; Andras Lorincz | ||||
| Title | Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures | Type | Conference Article | ||
| Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
| Volume | 173 | Issue |
Pages | 218-241 | |
| Keywords | |||||
| Abstract | Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures{’} preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots. | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | PMLR | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSJ2022 | Serial | 3769 | ||
| Permanent link to this record | |||||
| Author | Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez | ||||
| Title | MUST-VQA: MUltilingual Scene-text VQA | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 13804 | Issue |
Pages | 345–358 | |
| Keywords | Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models | ||||
| Abstract | In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. | ||||
| Address | Tel-Aviv; Israel; October 2022 | ||||
| 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 | ECCVW | ||
| Notes | DAG; 302.105; 600.155; 611.002 | Approved | no | ||
| Call Number | Admin @ si @ VBM2022 | Serial | 3770 | ||
| Permanent link to this record | |||||
| Author | Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas | ||||
| Title | Out-of-Vocabulary Challenge Report | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings European Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | 13804 | Issue |
Pages | 359–375 | |
| Keywords | |||||
| Abstract | This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. | ||||
| Address | Tel-Aviv; Israel; October 2022 | ||||
| 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 | ECCVW | ||
| Notes | DAG; 600.155; 302.105; 611.002 | Approved | no | ||
| Call Number | Admin @ si @ GMB2022 | Serial | 3771 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales | ||||
| Title | Deep Learning Based Shrimp Classification | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Symposium on Visual Computing | Abbreviated Journal | |
| Volume | 13598 | Issue |
Pages | 36–45 | |
| Keywords | Pigmentation; Color space; Light weight network | ||||
| Abstract | This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. | ||||
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| Area | Expedition | Conference | ISVC | ||
| Notes | MSIAU; no proj | Approved | no | ||
| Call Number | Admin @ si @ SAC2022 | Serial | 3772 | ||
| Permanent link to this record | |||||
| Author | Henry Velesaca; Patricia Suarez; Angel Sappa; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez | ||||
| Title | Review on Common Techniques for Urban Environment Video Analytics | Type | Conference Article | ||
| Year | 2022 | Publication | Anais do III Workshop Brasileiro de Cidades Inteligentes | Abbreviated Journal | |
| Volume | Issue |
Pages | 107-118 | ||
| Keywords | Video Analytics; Review; Urban Environments; Smart Cities | ||||
| Abstract | This work compiles the different computer vision-based approaches
from the state-of-the-art intended for video analytics in urban environments. The manuscript groups the different approaches according to the typical modules present in video analysis, including image preprocessing, object detection, classification, and tracking. This proposed pipeline serves as a basic guide to representing these most representative approaches in this topic of video analysis that will be addressed in this work. Furthermore, the manuscript is not intended to be an exhaustive review of the most advanced approaches, but only a list of common techniques proposed to address recurring problems in this field. |
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| Area | Expedition | Conference | WBCI | ||
| Notes | MSIAU; 601.349 | Approved | no | ||
| Call Number | Admin @ si @ VSS2022 | Serial | 3773 | ||
| Permanent link to this record | |||||
| Author | Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim | ||||
| Title | Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue |
Pages | 390-398 | ||
| Keywords | Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers | ||||
| Abstract | This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. | ||||
| Address | New Orleans; USA; June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RMV2022 | Serial | 3774 | ||
| Permanent link to this record | |||||
| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Jin Kim; Dogun Kim; Zhihao Li; Yingchun Jian; Bo Yan; Leilei Cao; Fengliang Qi; Hongbin Wang Rongyuan Wu; Lingchen Sun; Yongqiang Zhao; Lin Li; Kai Wang; Yicheng Wang; Xuanming Zhang; Huiyuan Wei; Chonghua Lv; Qigong Sun; Xiaolin Tian; Zhuang Jia; Jiakui Hu; Chenyang Wang; Zhiwei Zhong; Xianming Liu; Junjun Jiang | ||||
| Title | Thermal Image Super-Resolution Challenge Results – PBVS 2022 | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue |
Pages | 418-426 | ||
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| Abstract | This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. | ||||
| Address | New Orleans; USA; June 2022 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; no menciona | Approved | no | ||
| Call Number | Admin @ si @ RSV2022c | Serial | 3775 | ||
| Permanent link to this record | |||||