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Author Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji edit  url
doi  openurl
  Title Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding Type Conference Article
  Year 2022 Publication 29th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords Video coding; Quantization (signal); Computational modeling; Neural networks; Predictive models; Video compression; Syntactics  
  Abstract In video compression the luma channel can be useful for predicting chroma channels (Cb, Cr), as has been demonstrated with the Cross-Component Linear Model (CCLM) used in Versatile Video Coding (VVC) standard. More recently, it has been shown that neural networks can even better capture the relationship among different channels. In this paper, a new attention-based neural network is proposed for cross-component intra prediction. With the goal to simplify neural network design, the new framework consists of four branches: boundary branch and luma branch for extracting features from reference samples, attention branch for fusing the first two branches, and prediction branch for computing the predicted chroma samples. The proposed scheme is integrated into VVC test model together with one additional binary block-level syntax flag which indicates whether a given block makes use of the proposed method. Experimental results demonstrate 0.31%/2.36%/2.00% BD-rate reductions on Y/Cb/Cr components, respectively, on top of the VVC Test Model (VTM) 7.0 which uses CCLM.  
  Address Bordeaux; France; October 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 ICIP  
  Notes MACO Approved no  
  Call Number Admin @ si @ ZWM2022 Serial 3790  
Permanent link to this record
 
 
Author Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui edit  openurl
  Title Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data Type Conference Article
  Year 2022 Publication 10th International Conference on Learning Representations Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data.
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN) we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID.		  
  Address Virtual  
  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 ICLR  
  Notes LAMP; 600.147 Approved no  
  Call Number Admin @ si @ WWY2022 Serial 3791  
Permanent link to this record
 
 
Author Kai Wang; Fei Yang; Joost Van de Weijer edit   pdf
openurl 
  Title Attention Distillation: self-supervised vision transformer students need more guidance Type Conference Article
  Year 2022 Publication 33rd British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is therefore an important research topic. However, how to distill knowledge from one self-supervised ViT to another has not yet been explored. Moreover, the existing self-supervised knowledge distillation (SSKD) methods focus on ConvNet based architectures are suboptimal for ViT knowledge distillation. In this paper, we study knowledge distillation of self-supervised vision transformers (ViT-SSKD). We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between both. In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model). We are also the first to apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill is independent of self-supervised learning algorithms, it can be adapted to ViT based SSL methods to improve the performance in future research.  
  Address London; UK; November 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 BMVC  
  Notes LAMP; 600.147 Approved no  
  Call Number Admin @ si @ WYW2022 Serial 3793  
Permanent link to this record
 
 
Author Kai Wang; Chenshen Wu; Andrew Bagdanov; Xialei Liu; Shiqi Yang; Shangling Jui; Joost Van de Weijer edit  openurl
  Title Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification Type Conference Article
  Year 2022 Publication 33rd British Machine Vision Conference Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art.  
  Address London; UK; November 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 BMVC  
  Notes LAMP; 600.147 Approved no  
  Call Number Admin @ si @ WWB2022 Serial 3794  
Permanent link to this record
 
 
Author Vishwesh Pillai; Pranav Mehar; Manisha Das; Deep Gupta; Petia Radeva edit  url
doi  openurl
  Title Integrated Hierarchical and Flat Classifiers for Food Image Classification using Epistemic Uncertainty Type Conference Article
  Year 2022 Publication IEEE International Conference on Signal Processing and Communications Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract The problem of food image recognition is an essential one in today’s context because health conditions such as diabetes, obesity, and heart disease require constant monitoring of a person’s diet. To automate this process, several models are available to recognize food images. Due to a considerable number of unique food dishes and various cuisines, a traditional flat classifier ceases to perform well. To address this issue, prediction schemes consisting of both flat and hierarchical classifiers, with the analysis of epistemic uncertainty are used to switch between the classifiers. However, the accuracy of the predictions made using epistemic uncertainty data remains considerably low. Therefore, this paper presents a prediction scheme using three different threshold criteria that helps to increase the accuracy of epistemic uncertainty predictions. The performance of the proposed method is demonstrated using several experiments performed on the MAFood-121 dataset. The experimental results validate the proposal performance and show that the proposed threshold criteria help to increase the overall accuracy of the predictions by correctly classifying the uncertainty distribution of the samples.  
  Address Bangalore; India; July 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 SPCOM  
  Notes MILAB; no menciona Approved no  
  Call Number Admin @ si @ PMD2022 Serial 3796  
Permanent link to this record
 
 
Author Patricia Suarez; Dario Carpio; Angel Sappa; Henry Velesaca edit   pdf
url  doi
openurl 
  Title Transformer based Image Dehazing Type Conference Article
  Year 2022 Publication 16th IEEE International Conference on Signal Image Technology & Internet Based System Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords atmospheric light; brightness component; computational cost; dehazing quality; haze-free image  
  Abstract This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.  
  Address Dijon; France; October 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 SITIS  
  Notes MSIAU; no proj Approved no  
  Call Number Admin @ si @ SCS2022 Serial 3803  
Permanent link to this record
 
 
Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) edit  doi
isbn  openurl
  Title Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 Type Book Whole
  Year 2022 Publication Frontiers in Handwriting Recognition. Abbreviated Journal  
  Volume 13639 Issue Pages (up)  
  Keywords  
  Abstract  
  Address ICFHR 2022, Hyderabad, India, December 4–7, 2022  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor Utkarsh Porwal; Alicia Fornes; Faisal Shafait  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-21648-0 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ PFS2022 Serial 3809  
Permanent link to this record
 
 
Author Angel Sappa (ed) edit  doi
isbn  openurl
  Title ICT Applications for Smart Cities Type Book Whole
  Year 2022 Publication ICT Applications for Smart Cities Abbreviated Journal  
  Volume 224 Issue Pages (up)  
  Keywords Computational Intelligence; Intelligent Systems; Smart Cities; ICT Applications; Machine Learning; Pattern Recognition; Computer Vision; Image Processing  
  Abstract Part of the book series: Intelligent Systems Reference Library (ISRL)

This book is the result of four-year work in the framework of the Ibero-American Research Network TICs4CI funded by the CYTED program. In the following decades, 85% of the world's population is expected to live in cities; hence, urban centers should be prepared to provide smart solutions for problems ranging from video surveillance and intelligent mobility to the solid waste recycling processes, just to mention a few. More specifically, the book describes underlying technologies and practical implementations of several successful case studies of ICTs developed in the following smart city areas:

• Urban environment monitoring
• Intelligent mobility
• Waste recycling processes
• Video surveillance
• Computer-aided diagnose in healthcare systems
• Computer vision-based approaches for efficiency in production processes

The book is intended for researchers and engineers in the field of ICTs for smart cities, as well as to anyone who wants to know about state-of-the-art approaches and challenges on this field.		  
  Address September 2022  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor Angel Sappa  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title ISRL  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-06306-0 Medium  
  Area Expedition Conference  
  Notes MSIAU; MACO Approved no  
  Call Number Admin @ si @ Sap2022 Serial 3812  
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer edit   pdf
openurl 
  Title Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  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. Code is available in this https URL.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.147 Approved no  
  Call Number Admin @ si @ YWW2022b Serial 3815  
Permanent link to this record
 
 
Author Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas edit   pdf
url  openurl
  Title OCR-IDL: OCR Annotations for Industry Document Library Dataset Type Conference Article
  Year 2022 Publication ECCV Workshop on Text in Everything Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes DAG; no proj Approved no  
  Call Number Admin @ si @ BTG2022 Serial 3817  
Permanent link to this record
 
 
Author Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer edit  openurl
  Title One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a (
+1)-way classifier to predict the
source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show:
After source training, the resulting source model can get excellent performance for
;
After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method.
When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; no proj Approved no  
  Call Number Admin @ si @ YWW2022c Serial 3818  
Permanent link to this record
 
 
Author Saiping Zhang, Luis Herranz, Marta Mrak, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang edit   pdf
openurl 
  Title PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation-and Attention-based Network Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MACO; no proj Approved no  
  Call Number Admin @ si @ ZHM2022b Serial 3819  
Permanent link to this record
 
 
Author Ruben Ballester; Xavier Arnal Clemente; Carles Casacuberta; Meysam Madadi; Ciprian Corneanu edit   pdf
openurl 
  Title Towards explaining the generalization gap in neural networks using topological data analysis Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose, we compute homological persistence diagrams of weighted graphs constructed from neuron activation correlations after a training phase, aiming to capture patterns that are linked to the generalization capacity of the network. We compare the usefulness of different numerical summaries from persistence diagrams and show that a combination of some of them can accurately predict and partially explain the generalization gap without the need of a test set. Evaluation on two computer vision recognition tasks (CIFAR10 and SVHN) shows competitive generalization gap prediction when compared against state-of-the-art methods.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ BAC2022 Serial 3821  
Permanent link to this record
 
 
Author Arya Farkhondeh; Cristina Palmero; Simone Scardapane; Sergio Escalera edit   pdf
openurl 
  Title Towards Self-Supervised Gaze Estimation Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Recent joint embedding-based self-supervised methods have surpassed standard supervised approaches on various image recognition tasks such as image classification. These self-supervised methods aim at maximizing agreement between features extracted from two differently transformed views of the same image, which results in learning an invariant representation with respect to appearance and geometric image transformations. However, the effectiveness of these approaches remains unclear in the context of gaze estimation, a structured regression task that requires equivariance under geometric transformations (e.g., rotations, horizontal flip). In this work, we propose SwAT, an equivariant version of the online clustering-based self-supervised approach SwAV, to learn more informative representations for gaze estimation. We demonstrate that SwAT, with ResNet-50 and supported with uncurated unlabeled face images, outperforms state-of-the-art gaze estimation methods and supervised baselines in various experiments. In particular, we achieve up to 57% and 25% improvements in cross-dataset and within-dataset evaluation tasks on existing benchmarks (ETH-XGaze, Gaze360, and MPIIFaceGaze).  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ FPS2022 Serial 3822  
Permanent link to this record
 
 
Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit   pdf
openurl 
  Title Word separation in continuous sign language using isolated signs and post-processing Type Miscellaneous
  Year 2022 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLVID, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ RKE2022b Serial 3824  
Permanent link to this record
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