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Author | Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | Continual learning in cross-modal retrieval | Type | Conference Article | ||
Year | 2021 | Publication | 2nd CLVISION workshop | Abbreviated Journal | |
Volume | Issue | Pages | 3628-3638 | ||
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Abstract | Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline. | ||||
Address | Virtual; June 2021 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | LAMP; 600.120; 600.141; 600.147; 601.379 | Approved | no | ||
Call Number | Admin @ si @ WHW2021 | Serial | 3566 | ||
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Author | Vincenzo Lomonaco; Lorenzo Pellegrini; Andrea Cossu; Antonio Carta; Gabriele Graffieti; Tyler L. Hayes; Matthias De Lange; Marc Masana; Jary Pomponi; Gido van de Ven; Martin Mundt; Qi She; Keiland Cooper; Jeremy Forest; Eden Belouadah; Simone Calderara; German I. Parisi; Fabio Cuzzolin; Andreas Tolias; Simone Scardapane; Luca Antiga; Subutai Amhad; Adrian Popescu; Christopher Kanan; Joost Van de Weijer; Tinne Tuytelaars; Davide Bacciu; Davide Maltoni | ||||
Title | Avalanche: an End-to-End Library for Continual Learning | Type | Conference Article | ||
Year | 2021 | Publication | 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3595-3605 | ||
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Abstract | Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms. | ||||
Address | Virtual; June 2021 | ||||
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 | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ LPC2021 | Serial | 3567 | ||
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Author | Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 125-133 | ||
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Abstract | We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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Address | Virtual; January 2021 | ||||
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 | WACVW | ||
Notes | ADAS; 600.118; 600.124 | Approved | no | ||
Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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Author | Ozge Mercanoglu Sincan; Julio C. S. Jacques Junior; Sergio Escalera; Hacer Yalim Keles | ||||
Title | ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research | Type | Conference Article | ||
Year | 2021 | Publication | Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3467-3476 | ||
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Abstract | The performances of Sign Language Recognition (SLR) systems have improved considerably in recent years. However, several open challenges still need to be solved to allow SLR to be useful in practice. The research in the field is in its infancy in regards to the robustness of the models to a large diversity of signs and signers, and to fairness of the models to performers from different demographics. This work summarises the ChaLearn LAP Large Scale Signer Independent Isolated SLR Challenge, organised at CVPR 2021 with the goal of overcoming some of the aforementioned challenges. We analyse and discuss the challenge design, top winning solutions and suggestions for future research. The challenge attracted 132 participants in the RGB track and 59 in the RGB+Depth track, receiving more than 1.5K submissions in total. Participants were evaluated using a new large-scale multi-modal Turkish Sign Language (AUTSL) dataset, consisting of 226 sign labels and 36,302 isolated sign video samples performed by 43 different signers. Winning teams achieved more than 96% recognition rate, and their approaches benefited from pose/hand/face estimation, transfer learning, external data, fusion/ensemble of modalities and different strategies to model spatio-temporal information. However, methods still fail to distinguish among very similar signs, in particular those sharing similar hand trajectories. | ||||
Address | Virtual; June 2021 | ||||
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 | HuPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MJE2021 | Serial | 3560 | ||
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Author | Marc Masana; Tinne Tuytelaars; Joost Van de Weijer | ||||
Title | Ternary Feature Masks: zero-forgetting for task-incremental learning | Type | Conference Article | ||
Year | 2021 | Publication | 34th IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3565-3574 | ||
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Abstract | We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue -- and show experimentally -- that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task; with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches. | ||||
Address | Virtual; June 2021 | ||||
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 | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MTW2021 | Serial | 3565 | ||
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Author | Sudeep Katakol; Luis Herranz; Fei Yang; Marta Mrak | ||||
Title | DANICE: Domain adaptation without forgetting in neural image compression | Type | Conference Article | ||
Year | 2021 | Publication | Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 1921-1925 | ||
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Abstract | Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential functionalities. In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility with previously encoded bitstreams. Addressing these problems, we propose Codec Adaptation without Forgetting (CAwF), a framework that can avoid these problems by adding a small amount of custom parameters, where the source codec remains embedded and unchanged during the adaptation process. Experiments demonstrate its effectiveness and provide useful insights on the characteristics of catastrophic interference in NIC. | ||||
Address | Virtual; June 2021 | ||||
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 | LAMP; 600.120; 600.141; 601.379 | Approved | no | ||
Call Number | Admin @ si @ KHY2021 | Serial | 3568 | ||
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Author | Fei Yang; Luis Herranz; Yongmei Cheng; Mikhail Mozerov | ||||
Title | Slimmable compressive autoencoders for practical neural image compression | Type | Conference Article | ||
Year | 2021 | Publication | 34th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 4996-5005 | ||
Keywords | |||||
Abstract | Neural image compression leverages deep neural networks to outperform traditional image codecs in rate-distortion performance. However, the resulting models are also heavy, computationally demanding and generally optimized for a single rate, limiting their practical use. Focusing on practical image compression, we propose slimmable compressive autoencoders (SlimCAEs), where rate (R) and distortion (D) are jointly optimized for different capacities. Once trained, encoders and decoders can be executed at different capacities, leading to different rates and complexities. We show that a successful implementation of SlimCAEs requires suitable capacity-specific RD tradeoffs. Our experiments show that SlimCAEs are highly flexible models that provide excellent rate-distortion performance, variable rate, and dynamic adjustment of memory, computational cost and latency, thus addressing the main requirements of practical image compression. | ||||
Address | Virtual; June 2021 | ||||
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 | CVPR | ||
Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ YHC2021 | Serial | 3569 | ||
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Author | Arturo Fuentes; F. Javier Sanchez; Thomas Voncina; Jorge Bernal | ||||
Title | LAMV: Learning to Predict Where Spectators Look in Live Music Performances | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
Volume | 5 | Issue | Pages | 500-507 | |
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Abstract | The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends. | ||||
Address | Virtual; February 2021 | ||||
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 | MV; ISE; 600.119; | Approved | no | ||
Call Number | Admin @ si @ FSV2021 | Serial | 3570 | ||
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Author | Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
Title | Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12822 | Issue | Pages | 306-320 | |
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Abstract | This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ MRG2021b | Serial | 3571 | ||
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Author | Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
Title | Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12822 | Issue | Pages | 381–395 | |
Keywords | |||||
Abstract | In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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 | ICDAR | ||
Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ RMG2021 | Serial | 3572 | ||
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Author | Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal | ||||
Title | DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis | Type | Conference Article | ||
Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | 12823 | Issue | Pages | 555–568 | |
Keywords | |||||
Abstract | Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind. | ||||
Address | Lausanne; Suissa; September 2021 | ||||
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 | |||
Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
Call Number | Admin @ si @ BRL2021a | Serial | 3573 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation | Type | Conference Article | ||
Year | 2021 | Publication | 28th IEEE International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 19-22 | ||
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Abstract | This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. | ||||
Address | Anchorage-Alaska; USA; September 2021 | ||||
Corporate Author | Thesis | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICIP | ||
Notes | MSIAU; 600.130; 600.122; 601.349 | Approved | no | ||
Call Number | Admin @ si @ SSV2021b | Serial | 3579 | ||
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Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Sabari Nathan; Priya Kansal; Armin Mehri; Parichehr Behjati Ardakani; A.Dalal; A.Akula; D.Sharma; S.Pandey; B.Kumar; J.Yao; R.Wu; KFeng; N.Li; Y.Zhao; H.Patel; V. Chudasama; K.Pjajapati; A.Sarvaiya; K.Upla; K.Raja; R.Ramachandra; C.Bush; F.Almasri; T.Vandamme; O.Debeir; N.Gutierrez; Q.Nguyen; W.Beksi | ||||
Title | Thermal Image Super-Resolution Challenge – PBVS 2021 | Type | Conference Article | ||
Year | 2021 | Publication | Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 4359-4367 | ||
Keywords | |||||
Abstract | This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results outperformed those from the first challenge, thus showing an improvement in both evaluation metrics. | ||||
Address | Virtual; June 2021 | ||||
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; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ RSV2021 | Serial | 3581 | ||
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Author | Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa | ||||
Title | MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution | Type | Conference Article | ||
Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2703-2712 | ||
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Abstract | Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches. | ||||
Address | Virtual; January 2021 | ||||
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | WACV | ||
Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ MAS2021b | Serial | 3582 | ||
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Author | Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa | ||||
Title | LiNet: A Lightweight Network for Image Super Resolution | Type | Conference Article | ||
Year | 2021 | Publication | 25th International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 7196-7202 | ||
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Abstract | This paper proposes a new lightweight network, LiNet, that enhancing technical efficiency in lightweight super resolution and operating approximately like very large and costly networks in terms of number of network parameters and operations. The proposed architecture allows the network to learn more abstract properties by avoiding low-level information via multiple links. LiNet introduces a Compact Dense Module, which contains set of inner and outer blocks, to efficiently extract meaningful information, to better leverage multi-level representations before upsampling stage, and to allow an efficient information and gradient flow within the network. Experiments on benchmark datasets show that the proposed LiNet achieves favorable performance against lightweight state-of-the-art methods. | ||||
Address | Virtual; January 2021 | ||||
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 | |||
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Area | Expedition | Conference | |||
Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
Call Number | Admin @ si @ MAS2021a | Serial | 3583 | ||
Permanent link to this record |