| Records |
| Author |
Alina Matei; Andreea Glavan; Petia Radeva; Estefania Talavera |
| Title |
Towards Eating Habits Discovery in Egocentric Photo-Streams |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
| Volume |
9 |
Issue |
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Pages  |
17495-17506 |
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| Abstract |
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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MILAB; no proj |
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no |
| Call Number |
Admin @ si @ MGR2021 |
Serial |
3637 |
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| Author |
Yaxing Wang; Hector Laria Mantecon; Joost Van de Weijer; Laura Lopez-Fuentes; Bogdan Raducanu |
| Title |
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets |
Type |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
13990-13999 |
| Keywords |
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| Abstract |
Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points. |
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Virtual; October 2021 |
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ICCV |
| Notes |
LAMP; 600.147; 602.200; 600.120 |
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no |
| Call Number |
Admin @ si @ WLW2021 |
Serial |
3604 |
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| Author |
Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez |
| Title |
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision |
Type |
Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
23 |
Issue |
8 |
Pages |
12738-12751 |
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| Abstract |
Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences. |
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| Notes |
ADAS; 600.118 |
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no |
| Call Number |
Admin @ si @ GTS2021 |
Serial |
3598 |
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| Author |
Domicele Jonauskaite; Lucia Camenzind; C. Alejandro Parraga; Cecile N Diouf; Mathieu Mercapide Ducommun; Lauriane Müller; Melanie Norberg; Christine Mohr |
| Title |
Colour-emotion associations in individuals with red-green colour blindness |
Type |
Journal Article |
| Year |
2021 |
Publication |
PeerJ |
Abbreviated Journal |
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| Volume |
9 |
Issue |
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Pages |
e11180 |
| Keywords |
Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia. |
| Abstract |
Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient. |
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| Notes |
CIC; LAMP; 600.120; 600.128 |
Approved |
no |
| Call Number |
Admin @ si @ JCP2021 |
Serial |
3564 |
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| Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
| Title |
Modeling long-term interactions to enhance action recognition |
Type |
Conference Article |
| Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
10351-10358 |
| Keywords |
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| Abstract |
In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
| Address |
January 2021 |
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ICPR |
| Notes |
MILAB; |
Approved |
no |
| Call Number |
Admin @ si @ CRD2021 |
Serial |
3626 |
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| Author |
Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui |
| Title |
Generalized Source-free Domain Adaptation |
Type |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
8958-8967 |
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Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains. |
| Address |
Virtual; October 2021 |
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| Notes |
LAMP; 600.120; 600.147 |
Approved |
no |
| Call Number |
Admin @ si @ YWW2021 |
Serial |
3605 |
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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 |
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Issue |
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Pages |
7196-7202 |
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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 |
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| Notes |
MSIAU; 600.130; 600.122 |
Approved |
no |
| Call Number |
Admin @ si @ MAS2021a |
Serial |
3583 |
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| Author |
Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera |
| Title |
DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation |
Type |
Conference Article |
| Year |
2021 |
Publication |
19th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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Issue |
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Pages |
5471-5480 |
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We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. |
| Address |
Virtual; October 2021 |
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ICCV |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ BMT2021 |
Serial |
3606 |
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| Author |
Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor |
| Title |
A Few-shot Learning Approach for Historical Encoded Manuscript Recognition |
Type |
Conference Article |
| Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
5413-5420 |
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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. |
| Address |
Virtual; January 2021 |
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ICPR |
| Notes |
DAG; 600.121; 600.140 |
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no |
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Admin @ si @ SFK2021 |
Serial |
3449 |
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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 |
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4996-5005 |
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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 |
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CVPR |
| Notes |
LAMP; 600.120 |
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no |
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Admin @ si @ YHC2021 |
Serial |
3569 |
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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 |
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4359-4367 |
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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. |
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Virtual; June 2021 |
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CVPRW |
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MSIAU; 600.130; 600.122 |
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no |
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Admin @ si @ RSV2021 |
Serial |
3581 |
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| Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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no |
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Admin @ si @ MDB2021 |
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3491 |
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| Author |
Javier Marin; Sergio Escalera |
| Title |
SSSGAN: Satellite Style and Structure Generative Adversarial Networks |
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Journal Article |
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2021 |
Publication |
Remote Sensing |
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13 |
Issue |
19 |
Pages |
3984 |
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This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
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HUPBA; no proj |
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no |
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Admin @ si @ MaE2021 |
Serial |
3651 |
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| Author |
Kai Wang; Luis Herranz; Joost Van de Weijer |
| Title |
Continual learning in cross-modal retrieval |
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Conference Article |
| Year |
2021 |
Publication |
2nd CLVISION workshop |
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3628-3638 |
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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. |
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Virtual; June 2021 |
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LAMP; 600.120; 600.141; 600.147; 601.379 |
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no |
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Admin @ si @ WHW2021 |
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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 |
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Avalanche: an End-to-End Library for Continual Learning |
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Conference Article |
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2021 |
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34th IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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3595-3605 |
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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. |
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Virtual; June 2021 |
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LAMP; 600.120 |
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no |
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Admin @ si @ LPC2021 |
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3567 |
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