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Author  |
Alex Caralps | ||||
| Title | Estudi de viabilitat per la inspeccio automatica de cintes elastiques amb silicona | Type | Report | ||
| Year | 2000 | Publication | CVC Technical Report #45 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Car2000 | Serial | 346 | ||
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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 | Abbreviated Journal | |
| Volume | Issue | Pages | 146-157 | ||
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| Abstract | 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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| Area | Expedition | Conference | ICMR | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FSS2022 | Serial | 3808 | ||
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| Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title | Using the Average Landmark Vector Method for Robot Homing | Type | Conference Article | ||
| Year | 2007 | Publication | Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA | Abbreviated Journal | |
| Volume | 163 | Issue | Pages | 331–338 | |
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| ISSN | ISBN | 978–1–58603–798–7 | Medium | ||
| Area | Expedition | Conference | CCIA’07 | ||
| Notes | RV;ADAS | Approved | no | ||
| Call Number | Admin @ si @ GRL2007 | Serial | 899 | ||
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| Author |
Alex Gomez-Villa; Adrian Martin; Javier Vazquez; Marcelo Bertalmio; Jesus Malo | ||||
| Title | On the synthesis of visual illusions using deep generative models | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Vision | Abbreviated Journal | JOV |
| Volume | 22(8) | Issue | 2 | Pages | 1-18 |
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| Abstract | Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference. | ||||
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| Notes | LAMP; 600.161; 611.007 | Approved | no | ||
| Call Number | Admin @ si @ GMV2022 | Serial | 3682 | ||
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| Author |
Alex Gomez-Villa; Bartlomiej Twardowski; Kai Wang; Joost van de Weijer | ||||
| Title | Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning | Type | Conference Article | ||
| Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1690-1700 | ||
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| Abstract | Continuous unsupervised representation learning (CURL) research has greatly benefited from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL methods using SSL can learn high-quality representations without any labels, but with a notable performance drop when learning on a many-tasks data stream. We hypothesize that this is caused by the regularization losses that are imposed to prevent forgetting, leading to a suboptimal plasticity-stability trade-off: they either do not adapt fully to the incoming data (low plasticity), or incur significant forgetting when allowed to fully adapt to a new SSL pretext-task (low stability). In this work, we propose to train an expert network that is relieved of the duty of keeping the previous knowledge and can focus on performing optimally on the new tasks (optimizing plasticity). In the second phase, we combine this new knowledge with the previous network in an adaptation-retrospection phase to avoid forgetting and initialize a new expert with the knowledge of the old network. We perform several experiments showing that our proposed approach outperforms other CURL exemplar-free methods in few- and many-task split settings. Furthermore, we show how to adapt our approach to semi-supervised continual learning (Semi-SCL) and show that we surpass the accuracy of other exemplar-free Semi-SCL methods and reach the results of some others that use exemplars. | ||||
| Address | Waikoloa; Hawai; USA; January 2024 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ GTW2024 | Serial | 3989 | ||
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| Author |
Alex Gomez-Villa; Bartlomiej Twardowski; Lu Yu; Andrew Bagdanov; Joost Van de Weijer | ||||
| Title | Continually Learning Self-Supervised Representations With Projected Functional Regularization | Type | Conference Article | ||
| Year | 2022 | Publication | CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) | Abbreviated Journal | |
| Volume | Issue | Pages | 3866-3876 | ||
| Keywords | Computer vision; Conferences; Self-supervised learning; Image representation; Pattern recognition | ||||
| Abstract | Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally – they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay
mechanism. We show that naive functional regularization,also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in different scenarios and on multiple datasets. |
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| Address | New Orleans, USA; 20 June 2022 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP: 600.147; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ GTY2022 | Serial | 3704 | ||
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| Author |
Alex Pardo; Albert Clapes; Sergio Escalera; Oriol Pujol | ||||
| Title | Actions in Context: System for people with Dementia | Type | Conference Article | ||
| Year | 2013 | Publication | 2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 3-14 | ||
| Keywords | Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia | ||||
| Abstract | In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios. | ||||
| Address | Barcelona; September 2013 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| ISSN | 0302-9743 | ISBN | 978-3-319-04177-3 | Medium | |
| Area | Expedition | Conference | ECCS | ||
| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ PCE2013 | Serial | 2354 | ||
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| Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun | ||||
| Title | CARLA: An Open Urban Driving Simulator | Type | Conference Article | ||
| Year | 2017 | Publication | 1st Annual Conference on Robot Learning. Proceedings of Machine Learning | Abbreviated Journal | |
| Volume | 78 | Issue | Pages | 1-16 | |
| Keywords | Autonomous driving; sensorimotor control; simulation | ||||
| Abstract | We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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| Address | Mountain View; CA; USA; November 2017 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes | ADAS; 600.085; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DRC2017 | Serial | 2988 | ||
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| Author |
Alfons Juan-Ciscar; Gemma Sanchez | ||||
| Title | PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008 | Type | Book Whole | ||
| Year | 2008 | Publication | Abbreviated Journal | ||
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| Address | Barcelona (Spain) | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ JuS2008 | Serial | 1054 | ||
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| Author |
Ali Furkan Biten | ||||
| Title | A Bitter-Sweet Symphony on Vision and Language: Bias and World Knowledge | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Vision and Language are broadly regarded as cornerstones of intelligence. Even though language and vision have different aims – language having the purpose of communication, transmission of information and vision having the purpose of constructing mental representations around us to navigate and interact with objects – they cooperate and depend on one another in many tasks we perform effortlessly. This reliance is actively being studied in various Computer Vision tasks, e.g. image captioning, visual question answering, image-sentence retrieval, phrase grounding, just to name a few. All of these tasks share the inherent difficulty of the aligning the two modalities, while being robust to language
priors and various biases existing in the datasets. One of the ultimate goal for vision and language research is to be able to inject world knowledge while getting rid of the biases that come with the datasets. In this thesis, we mainly focus on two vision and language tasks, namely Image Captioning and Scene-Text Visual Question Answering (STVQA). In both domains, we start by defining a new task that requires the utilization of world knowledge and in both tasks, we find that the models commonly employed are prone to biases that exist in the data. Concretely, we introduce new tasks and discover several problems that impede performance at each level and provide remedies or possible solutions in each chapter: i) We define a new task to move beyond Image Captioning to Image Interpretation that can utilize Named Entities in the form of world knowledge. ii) We study the object hallucination problem in classic Image Captioning systems and develop an architecture-agnostic solution. iii) We define a sub-task of Visual Question Answering that requires reading the text in the image (STVQA), where we highlight the limitations of current models. iv) We propose an architecture for the STVQA task that can point to the answer in the image and show how to combine it with classic VQA models. v) We show how far language can get us in STVQA and discover yet another bias which causes the models to disregard the image while doing Visual Question Answering. |
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| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez | |
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| ISSN | ISBN | 978-84-124793-5-5 | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Bit2022 | Serial | 3755 | ||
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| Author |
Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching | Type | Conference Article | ||
| Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1391-1400 | ||
| Keywords | Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning | ||||
| Abstract | The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin. | ||||
| Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.155; 302.105; | Approved | no | ||
| Call Number | Admin @ si @ BMG2022 | Serial | 3663 | ||
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| Author |
Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning | Type | Conference Article | ||
| Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1381-1390 | ||
| Keywords | Measurement; Training; Visualization; Analytical models; Computer vision; Computational modeling; Training data | ||||
| Abstract | Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase
in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models’ object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights are available online. |
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| Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG; 600.155; 302.105 | Approved | no | ||
| Call Number | Admin @ si @ BGK2022 | Serial | 3662 | ||
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| Author |
Ali Furkan Biten; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas | ||||
| Title | Good News, Everyone! Context driven entity-aware captioning for news images | Type | Conference Article | ||
| Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 12458-12467 | ||
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| Abstract | Current image captioning systems perform at a merely descriptive level, essentially enumerating the objects in the scene and their relations. Humans, on the contrary, interpret images by integrating several sources of prior knowledge of the world. In this work, we aim to take a step closer to producing captions that offer a plausible interpretation of the scene, by integrating such contextual information into the captioning pipeline. For this we focus on the captioning of images used to illustrate news articles. We propose a novel captioning method that is able to leverage contextual information provided by the text of news articles associated with an image. Our model is able to selectively draw information from the article guided by visual cues, and to dynamically extend the output dictionary to out-of-vocabulary named entities that appear in the context source. Furthermore we introduce“ GoodNews”, the largest news image captioning dataset in the literature and demonstrate state-of-the-art results. | ||||
| Address | Long beach; California; USA; june 2019 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | DAG; 600.129; 600.135; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BGR2019 | Serial | 3289 | ||
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| Author |
Ali Furkan Biten; Ruben Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | Scene Text Visual Question Answering | Type | Conference Article | ||
| Year | 2019 | Publication | 18th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 4291-4301 | ||
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| Abstract | Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting highlevel semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research. | ||||
| Address | Seul; Corea; October 2019 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | DAG; 600.129; 600.135; 601.338; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BTM2019b | Serial | 3285 | ||
| Permanent link to this record | |||||
| Author |
Ali Furkan Biten; Ruben Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | ICDAR 2019 Competition on Scene Text Visual Question Answering | Type | Conference Article | ||
| Year | 2019 | Publication | 3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019 | Abbreviated Journal | |
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| Abstract | This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios. The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that can exploit scene text to achieve holistic image understanding. |
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| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | CLVL | ||
| Notes | DAG; 600.129; 601.338; 600.135; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BTM2019a | Serial | 3284 | ||
| Permanent link to this record | |||||