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Author Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol edit   pdf
url  openurl
  Title STEP – Towards Structured Scene-Text Spotting Type Conference Article
  Year (down) 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 883-892  
  Keywords  
  Abstract We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios.  
  Address Waikoloa; Hawai; USA; January 2024  
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  Area Expedition Conference WACV  
  Notes DAG Approved no  
  Call Number Admin @ si @ GKR2024 Serial 3992  
Permanent link to this record
 
 
Author Adarsh Tiwari; Sanket Biswas; Josep Llados edit  url
openurl 
  Title Can Pre-trained Language Models Help in Understanding Handwritten Symbols? Type Conference Article
  Year (down) 2023 Publication 17th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 14193 Issue Pages 199–211  
  Keywords  
  Abstract The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets.  
  Address San Jose; CA; USA; August 2023  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ TBL2023 Serial 3908  
Permanent link to this record
 
 
Author Adrien Pavao; Isabelle Guyon; Anne-Catherine Letournel; Dinh-Tuan Tran; Xavier Baro; Hugo Jair Escalante; Sergio Escalera; Tyler Thomas; Zhen Xu edit  url
openurl 
  Title CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges Type Journal Article
  Year (down) 2023 Publication Journal of Machine Learning Research Abbreviated Journal JMLR  
  Volume Issue Pages  
  Keywords  
  Abstract CodaLab Competitions is an open source web platform designed to help data scientists and research teams to crowd-source the resolution of machine learning problems through the organization of competitions, also called challenges or contests. CodaLab Competitions provides useful features such as multiple phases, results and code submissions, multi-score leaderboards, and jobs running
inside Docker containers. The platform is very flexible and can handle large scale experiments, by allowing organizers to upload large datasets and provide their own CPU or GPU compute workers.		  
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  Notes HUPBA Approved no  
  Call Number Admin @ si @ PGL2023 Serial 3973  
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Author Ahmed Salih; Ilaria Boscolo Galazzo; Zahra Raisi Estabragh; Steffen E Petersen; Gloria Menegaz; Petia Radeva edit   pdf
url  openurl
  Title Characterizing the contribution of dependent features in XAI methods Type Miscellaneous
  Year (down) 2023 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpretability of models and makes the models more trustworthy and transparent. In this context, many XAI methods were proposed being SHAP and LIME the most popular. However, the proposed methods assume that used predictors in the machine learning models are independent which in general is not necessarily true. Such assumption casts shadows on the robustness of the XAI outcomes such as the list of informative predictors. Here, we propose a simple, yet useful proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the predictors. The proposed approach has the advantage of being model-agnostic as well as simple to calculate the impact of each predictor in the model in presence of collinearity.  
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  Notes MILAB Approved no  
  Call Number Admin @ si @ SBR2023 Serial 3868  
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Author Akhil Gurram; Antonio Lopez edit   pdf
url  openurl
  Title On the Metrics for Evaluating Monocular Depth Estimation Type Miscellaneous
  Year (down) 2023 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (AVs) or driver assistance. Therefore, a relevant arising question is whether the standard metrics for MDE assessment are a good indicator of the accuracy of future MDE-based driving-related perception tasks. We address this question in this paper. In particular, we take the task of 3D object detection on point clouds as a proxy of on-board perception. We train and test state-of-the-art 3D object detectors using 3D point clouds coming from MDE models. We confront the ranking of object detection results with the ranking given by the depth estimation metrics of the MDE models. We conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods that reflects relatively well the 3D object detection results we may expect. Among the different metrics, the absolute relative (abs-rel) error seems to be the best for that purpose.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ GuL2023 Serial 3867  
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Author Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer edit  doi
openurl 
  Title Generative Multi-Label Zero-Shot Learning Type Journal Article
  Year (down) 2023 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 45 Issue 12 Pages 14611-14624  
  Keywords Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis  
  Abstract Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods.  
  Address December 2023  
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  Notes LAMP; PID2021-128178OB-I00 Approved no  
  Call Number Admin @ si @ Serial 3853  
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Author Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva edit  url
openurl 
  Title CCLM: Class-Conditional Label Noise Modelling Type Conference Article
  Year (down) 2023 Publication 11th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 14062 Issue Pages 3-14  
  Keywords  
  Abstract The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness.  
  Address Alicante; Spain; June 2023  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
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  ISSN ISBN Medium  
  Area Expedition Conference IbPRIA  
  Notes MILAB Approved no  
  Call Number Admin @ si @ TNM2023 Serial 3925  
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Author Albin Soutif; Antonio Carta; Andrea Cossu; Julio Hurtado; Hamed Hemati; Vincenzo Lomonaco; Joost Van de Weijer edit   pdf
url  openurl
  Title A Comprehensive Empirical Evaluation on Online Continual Learning Type Conference Article
  Year (down) 2023 Publication Visual Continual Learning (ICCV-W) Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream. In this empirical evaluation, we evaluate various methods from the literature that tackle online continual learning. More specifically, we focus on the class-incremental setting in the context of image classification, where the learner must learn new classes incrementally from a stream of data. We compare these methods on the Split-CIFAR100 and Split-TinyImagenet benchmarks, and measure their average accuracy, forgetting, stability, and quality of the representations, to evaluate various aspects of the algorithm at the end but also during the whole training period. We find that most methods suffer from stability and underfitting issues. However, the learned representations are comparable to i.i.d. training under the same computational budget. No clear winner emerges from the results and basic experience replay, when properly tuned and implemented, is a very strong baseline. We release our modular and extensible codebase at this https URL based on the avalanche framework to reproduce our results and encourage future research.  
  Address Paris; France; October 2023  
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  Area Expedition Conference ICCVW  
  Notes LAMP Approved no  
  Call Number Admin @ si @ SCC2023 Serial 3938  
Permanent link to this record
 
 
Author Albin Soutif; Antonio Carta; Joost Van de Weijer edit   pdf
url  openurl
  Title Improving Online Continual Learning Performance and Stability with Temporal Ensembles Type Conference Article
  Year (down) 2023 Publication 2nd Conference on Lifelong Learning Agents Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Neural networks are very effective when trained on large datasets for a large number of iterations. However, when they are trained on non-stationary streams of data and in an online fashion, their performance is reduced (1) by the online setup, which limits the availability of data, (2) due to catastrophic forgetting because of the non-stationary nature of the data. Furthermore, several recent works (Caccia et al., 2022; Lange et al., 2023) arXiv:2205.13452 showed that replay methods used in continual learning suffer from the stability gap, encountered when evaluating the model continually (rather than only on task boundaries). In this article, we study the effect of model ensembling as a way to improve performance and stability in online continual learning. We notice that naively ensembling models coming from a variety of training tasks increases the performance in online continual learning considerably. Starting from this observation, and drawing inspirations from semi-supervised learning ensembling methods, we use a lightweight temporal ensemble that computes the exponential moving average of the weights (EMA) at test time, and show that it can drastically increase the performance and stability when used in combination with several methods from the literature.  
  Address Montreal; Canada; August 2023  
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  Area Expedition Conference COLLAS  
  Notes LAMP Approved no  
  Call Number Admin @ si @ SCW2023 Serial 3922  
Permanent link to this record
 
 
Author Alejandro Ariza-Casabona; Bartlomiej Twardowski; Tri Kurniawan Wijaya edit  url
openurl 
  Title Exploiting Graph Structured Cross-Domain Representation for Multi-domain Recommendation Type Conference Article
  Year (down) 2023 Publication European Conference on Information Retrieval – ECIR 2023: Advances in Information Retrieval Abbreviated Journal  
  Volume 13980 Issue Pages 49–65  
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  Abstract Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. Both can be achieved by introducing a specific modeling of input data (i.e. disjoint history) or trying dedicated training regimes. At the same time, treating domains as separate input sources becomes a limitation as it does not capture the interplay that naturally exists between domains. In this work, we efficiently learn multi-domain representation of sequential users’ interactions using graph neural networks. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec (short for Multi-dom Ain Graph-based Recommender). To better capture all relations in a multi-domain setting, we learn two graph-based sequential representations simultaneously: domain-guided for recent user interest, and general for long-term interest. This approach helps to mitigate the negative knowledge transfer problem from multiple domains and improve overall representation. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods. Furthermore, we provide an ablation study and discuss further extensions of our method.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
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  ISSN ISBN Medium  
  Area Expedition Conference ECIR  
  Notes LAMP Approved no  
  Call Number Admin @ si @ ATK2023 Serial 3933  
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