| Records |
| Author |
Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera |
| Title |
Multi-varied Cumulative Alignment for Domain Adaptation |
Type |
Conference Article |
| Year |
2022 |
Publication |
6th International Conference on Image Analysis and Processing |
Abbreviated Journal |
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| Volume |
13232 |
Issue |
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Pages |
324–334 |
| Keywords |
Domain Adaptation; Computer vision; Neural networks |
| Abstract |
Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca). |
| Address |
Indonesia; October 2022 |
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ICIAP |
| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ OAS2022 |
Serial |
3777 |
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| Author |
Aitor Alvarez-Gila; Joost Van de Weijer; Yaxing Wang; Estibaliz Garrote |
| Title |
MVMO: A Multi-Object Dataset for Wide Baseline Multi-View Semantic Segmentation |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Issue |
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Pages |
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| Keywords |
multi-view; cross-view; semantic segmentation; synthetic dataset |
| Abstract |
We present MVMO (Multi-View, Multi-Object dataset): a synthetic dataset of 116,000 scenes containing randomly placed objects of 10 distinct classes and captured from 25 camera locations in the upper hemisphere. MVMO comprises photorealistic, path-traced image renders, together with semantic segmentation ground truth for every view. Unlike existing multi-view datasets, MVMO features wide baselines between cameras and high density of objects, which lead to large disparities, heavy occlusions and view-dependent object appearance. Single view semantic segmentation is hindered by self and inter-object occlusions that could benefit from additional viewpoints. Therefore, we expect that MVMO will propel research in multi-view semantic segmentation and cross-view semantic transfer. We also provide baselines that show that new research is needed in such fields to exploit the complementary information of multi-view setups 1 . |
| Address |
Bordeaux; France; October2022 |
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LAMP |
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no |
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Admin @ si @ AWW2022 |
Serial |
3781 |
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| Author |
Ahmed M. A. Salih; Ilaria Boscolo Galazzo; Federica Cruciani; Lorenza Brusini; Petia Radeva |
| Title |
Investigating Explainable Artificial Intelligence for MRI-based Classification of Dementia: a New Stability Criterion for Explainable Methods |
Type |
Conference Article |
| Year |
2022 |
Publication |
29th IEEE International Conference on Image Processing |
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| Keywords |
Image processing; Stability criteria; Machine learning; Robustness; Alzheimer's disease; Monitoring |
| Abstract |
Individuals diagnosed with Mild Cognitive Impairment (MCI) have shown an increased risk of developing Alzheimer’s Disease (AD). As such, early identification of dementia represents a key prognostic element, though hampered by complex disease patterns. Increasing efforts have focused on Machine Learning (ML) to build accurate classification models relying on a multitude of clinical/imaging variables. However, ML itself does not provide sensible explanations related to the model mechanism and feature contribution. Explainable Artificial Intelligence (XAI) represents the enabling technology in this framework, allowing to understand ML outcomes and derive human-understandable explanations. In this study, we aimed at exploring ML combined with MRI-based features and XAI to solve this classification problem and interpret the outcome. In particular, we propose a new method to assess the robustness of feature rankings provided by XAI methods, especially when multicollinearity exists. Our findings indicate that our method was able to disentangle the list of the informative features underlying dementia, with important implications for aiding personalized monitoring plans. |
| Address |
Bordeaux; France; October 2022 |
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ICIP |
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MILAB |
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no |
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Admin @ si @ SBC2022 |
Serial |
3789 |
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| Author |
Chengyi Zou; Shuai Wan; Marta Mrak; Marc Gorriz Blanch; Luis Herranz; Tiannan Ji |
| 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 |
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Issue |
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Pages |
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| 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 |
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ICIP |
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MACO |
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no |
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Admin @ si @ ZWM2022 |
Serial |
3790 |
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| Author |
Yaxing Wang; Joost Van de Weijer; Lu Yu; Shangling Jui |
| 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 |
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Pages |
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| 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 |
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ICLR |
| Notes |
LAMP; 600.147 |
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no |
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Admin @ si @ WWY2022 |
Serial |
3791 |
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| 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 |
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Pages |
146-157 |
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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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ICMR |
| Notes |
HuPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ FSS2022 |
Serial |
3808 |
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| Author |
Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal |
| Title |
DocEnTr: An End-to-End Document Image Enhancement Transformer |
Type |
Conference Article |
| Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
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Issue |
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Pages |
1699-1705 |
| Keywords |
Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads |
| Abstract |
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR |
| Address |
August 21-25, 2022 , Montréal Québec |
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ICPR |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
| Call Number |
Admin @ si @ SBJ2022 |
Serial |
3730 |
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| Author |
Carlos Boned Riera; Oriol Ramos Terrades |
| Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
Type |
Conference Article |
| Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
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2186-2191 |
| Keywords |
Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
| Abstract |
Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
| Address |
Montreal; Quebec; Canada; August 2022 |
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ICPR |
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DAG; 600.121; 600.162 |
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no |
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Admin @ si @ BoR2022 |
Serial |
3741 |
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| Author |
Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu |
| Title |
Visual Transformers with Primal Object Queries for Multi-Label Image Classification |
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Conference Article |
| Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
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Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively. |
| Address |
Montreal; Quebec; Canada; August 2022 |
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ICPR |
| Notes |
LAMP; 600.147; 601.309 |
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no |
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Admin @ si @ YWY2022 |
Serial |
3786 |
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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 |
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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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DAG; |
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no |
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Admin @ si @ BSA2022 |
Serial |
3788 |
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| Author |
Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados |
| Title |
Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis |
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Conference Article |
| Year |
2022 |
Publication |
Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
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13424 |
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336-348 |
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Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk |
| Abstract |
Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. |
| Address |
June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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IGS |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Admin @ si @ BFC2022 |
Serial |
3738 |
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| Author |
Alicia Fornes; Asma Bensalah; Cristina Carmona_Duarte; Jialuo Chen; Miguel A. Ferrer; Andreas Fischer; Josep Llados; Cristina Martin; Eloy Opisso; Rejean Plamondon; Anna Scius-Bertrand; Josep Maria Tormos |
| Title |
The RPM3D Project: 3D Kinematics for Remote Patient Monitoring |
Type |
Conference Article |
| Year |
2022 |
Publication |
Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 |
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13424 |
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217-226 |
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Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics |
| Abstract |
This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute (https://www.guttmann.com/en/) (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases. |
| Address |
June 7-9, 2022, Las Palmas de Gran Canaria, Spain |
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IGS |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
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no |
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Admin @ si @ FBC2022 |
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3739 |
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| Author |
Patricia Suarez; Angel Sappa; Dario Carpio; Henry Velesaca; Francisca Burgos; Patricia Urdiales |
| Title |
Deep Learning Based Shrimp Classification |
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Conference Article |
| Year |
2022 |
Publication |
17th International Symposium on Visual Computing |
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13598 |
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36–45 |
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Pigmentation; Color space; Light weight network |
| Abstract |
This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model. |
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ISVC |
| Notes |
MSIAU; no proj |
Approved |
no |
| Call Number |
Admin @ si @ SAC2022 |
Serial |
3772 |
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| Author |
Javier Rodenas; Bhalaji Nagarajan; Marc Bolaños; Petia Radeva |
| Title |
Learning Multi-Subset of Classes for Fine-Grained Food Recognition |
Type |
Conference Article |
| Year |
2022 |
Publication |
7th International Workshop on Multimedia Assisted Dietary Management |
Abbreviated Journal |
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Pages |
17–26 |
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| Abstract |
Food image recognition is a complex computer vision task, because of the large number of fine-grained food classes. Fine-grained recognition tasks focus on learning subtle discriminative details to distinguish similar classes. In this paper, we introduce a new method to improve the classification of classes that are more difficult to discriminate based on Multi-Subsets learning. Using a pre-trained network, we organize classes in multiple subsets using a clustering technique. Later, we embed these subsets in a multi-head model structure. This structure has three distinguishable parts. First, we use several shared blocks to learn the generalized representation of the data. Second, we use multiple specialized blocks focusing on specific subsets that are difficult to distinguish. Lastly, we use a fully connected layer to weight the different subsets in an end-to-end manner by combining the neuron outputs. We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets. Our method was successful in learning the confused classes better and we outperformed the state-of-the-art on the three datasets. |
| Address |
Lisboa; Portugal; October 2022 |
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MADiMa |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ RNB2022 |
Serial |
3797 |
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| Author |
Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera |
| Title |
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging |
Type |
Conference Article |
| Year |
2022 |
Publication |
International MICCAI Brainlesion Workshop |
Abbreviated Journal |
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| Volume |
12963 |
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Pages |
540–551 |
| Keywords |
Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN |
| Abstract |
In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1. |
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LNCS |
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MICCAIW |
| Notes |
HUPBA; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ JOM2022 |
Serial |
3800 |
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