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| Author | Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados | ||||
| Title | TWD: A New Deep E2E Model for Text Watermark Detection in Video Images | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection | ||||
| Abstract | Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge | ||||
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Montreal; Quebec; Canada; August 2022 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; | Approved | no | ||
| Call Number | Admin @ si @ BSA2022 | Serial | 3788 | ||
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| Author | Mohamed Ramzy Ibrahim; Robert Benavente; Felipe Lumbreras; Daniel Ponsa | ||||
| Title | 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks | Type | Conference Article | ||
| Year | 2022 | Publication | CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition | ||||
| Abstract | The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively. | ||||
| Address |
New Orleans, USA; 19 June 2022 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.130 | Approved | no | ||
| Call Number | Admin @ si @ IBL2022 | Serial | 3693 | ||
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| Author | Kai Wang; Xialei Liu; Andrew Bagdanov; Luis Herranz; Shangling Jui; Joost Van de Weijer | ||||
| Title | Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition | Type | Conference Article | ||
| Year | 2022 | Publication | CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | 3728-3738 | ||
| Keywords | Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis | ||||
| Abstract | In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively. |
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| Address |
New Orleans, USA; 20 June 2022 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ WLB2022 | Serial | 3686 | ||
| Permanent link to this record | |||||
| Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta | ||||
| Title | Area Under the ROC Curve Maximization for Metric Learning | Type | Conference Article | ||
| Year | 2022 | Publication | CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition | ||||
| Abstract | Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification. | ||||
| Address |
New Orleans, USA; 20 June 2022 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | CIC; LAMP; | Approved | no | ||
| Call Number | Admin @ si @ GAB2022 | Serial | 3700 | ||
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| Author | Alex Gomez-Villa; Bartlomiej Twardowski; Lu Yu; Andrew Bagdanov; Joost Van de Weijer | ||||
| Title | Continually Learning Self-Supervised Representations With Projected Functional Regularization | Type | Conference Article | ||
| Year | 2022 | Publication | CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | 3866-3876 | ||
| Keywords | Computer vision; Conferences; Self-supervised learning; Image representation; Pattern recognition | ||||
| Abstract | Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally – they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay
mechanism. We show that naive functional regularization,also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in different scenarios and on multiple datasets. |
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| Address |
New Orleans, USA; 20 June 2022 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP: 600.147; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ GTY2022 | Serial | 3704 | ||
| 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 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ LNS2022 | Serial | 3768 | ||
| 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 | |||||
| Author | Francesco Pelosin; Saurav Jha; Andrea Torsello; Bogdan Raducanu; Joost Van de Weijer | ||||
| Title | Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Learning systems; Weight measurement; Image recognition; Surgery; Benchmark testing; Transformers; Stability analysis | ||||
| Abstract | In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) – while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners. 1 | ||||
| Address |
New Orleans; USA; June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; 600.147 | Approved | no | ||
| Call Number | Admin @ si @ PJT2022 | Serial | 3784 | ||
| Permanent link to this record | |||||
| Author | Hector Laria Mantecon; Yaxing Wang; Joost Van de Weijer; Bogdan Raducanu | ||||
| Title | Transferring Unconditional to Conditional GANs With Hyper-Modulation | Type | Conference Article | ||
| Year | 2022 | Publication | IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) | Abbreviated Journal | |
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| Abstract | GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. | ||||
| Address |
New Orleans; USA; June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; 600.147; 602.200 | Approved | no | ||
| Call Number | LWW2022a | Serial | 3785 | ||
| Permanent link to this record | |||||
| Author | Alex Falcon; Swathikiran Sudhakaran; Giuseppe Serra; Sergio Escalera; Oswald Lanz | ||||
| Title | Relevance-based Margin for Contrastively-trained Video Retrieval Models | Type | Conference Article | ||
| Year | 2022 | Publication | ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval | Abbreviated Journal | |
| Volume | Issue | Pages | 146-157 | ||
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| Abstract | Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22. | ||||
| Address |
Newwark, NJ, USA, 27 June 2022 | ||||
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| Area | Expedition | Conference | ICMR | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSS2022 | Serial | 3808 | ||
| Permanent link to this record | |||||
| Author | Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang | ||||
| Title | Improving Handwritten Music Recognition through Language Model Integration | Type | Conference Article | ||
| Year | 2022 | Publication | 4th International Workshop on Reading Music Systems (WoRMS2022) | Abbreviated Journal | |
| Volume | Issue | Pages | 42-46 | ||
| Keywords | optical music recognition; historical sources; diversity; music theory; digital humanities | ||||
| Abstract | Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. | ||||
| Address |
November 18, 2022 | ||||
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| Area | Expedition | Conference | WoRMS | ||
| Notes | DAG; 600.121; 600.162; 602.230 | Approved | no | ||
| Call Number | Admin @ si @ TBF2022 | Serial | 3735 | ||
| 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 | ||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANR2022 | Serial | 3763 | ||
| Permanent link to this record | |||||
| Author | Jorge Charco; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Human Pose Estimation through a Novel Multi-view Scheme | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 855-862 | |
| Keywords | Multi-view Scheme; Human Pose Estimation; Relative Camera Pose; Monocular Approach | ||||
| Abstract | This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations. |
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| Address |
On line; Feb 6, 2022 – Feb 8, 2022 | ||||
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| ISSN | 2184-4321 | ISBN | 978-989-758-555-5 | Medium | |
| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU; 600.160 | Approved | no | ||
| Call Number | Admin @ si @ CSV2022 | Serial | 3689 | ||
| Permanent link to this record | |||||
| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Multi-Image Super-Resolution for Thermal Images | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 635-642 | |
| Keywords | Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block | ||||
| Abstract | This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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| Address |
Online; Feb 6-8, 2022 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MSIAU; 601.349 | Approved | no | ||
| Call Number | Admin @ si @ RSV2022a | Serial | 3690 | ||
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