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Author |
David Berga; Marc Masana; Joost Van de Weijer |
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Title |
Disentanglement of Color and Shape Representations for Continual Learning |
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Conference Article |
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2020 |
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ICML Workshop on Continual Learning |
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We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. |
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Virtual; July 2020 |
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ICMLW |
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LAMP; 600.120 |
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no |
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Admin @ si @ BMW2020 |
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3506 |
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Author |
David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
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Title |
Automated Prediction of Preferences Using Facial Expressions |
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Journal Article |
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Year |
2014 |
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PloS one |
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Plos |
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9 |
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2 |
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e87434 |
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We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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OR;MV |
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no |
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Admin @ si @ MNT2014 |
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2453 |
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Author |
Axel Barroso-Laguna; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk |
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Title |
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters |
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Conference Article |
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Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
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5835-5843 |
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We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity. |
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Seul; Corea; October 2019 |
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ICCV |
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MSIAU; 600.122 |
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no |
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Admin @ si @ BRP2019 |
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3290 |
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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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Year |
2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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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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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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no |
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Admin @ si @ DRC2017 |
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2988 |
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Author |
Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Debora Gil; Cristina Rodriguez de Miguel; Fernando Vilariño |
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Title |
WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians |
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Journal Article |
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Year |
2015 |
Publication |
Computerized Medical Imaging and Graphics |
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CMIG |
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43 |
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99-111 |
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Keywords |
Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection |
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We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. |
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0895-6111 |
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MV; IAM; 600.047; 600.060; 600.075;SIAI |
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no |
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Admin @ si @ BSF2015 |
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2609 |
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Author |
Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat |
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Title |
Weakly Supervised Multi-Object Tracking and Segmentation |
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Conference Article |
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Year |
2021 |
Publication |
IEEE Winter Conference on Applications of Computer Vision Workshops |
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125-133 |
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We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by
Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the
objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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Virtual; January 2021 |
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WACVW |
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ADAS; 600.118; 600.124 |
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no |
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Admin @ si @ RPR2021 |
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3548 |
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Author |
Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol |
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Title |
STEP – Towards Structured Scene-Text Spotting |
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Conference Article |
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Year |
2024 |
Publication |
Winter Conference on Applications of Computer Vision |
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883-892 |
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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. |
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Waikoloa; Hawai; USA; January 2024 |
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WACV |
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DAG |
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no |
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Admin @ si @ GKR2024 |
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3992 |
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Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane |
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Title |
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains |
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Miscellaneous |
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2024 |
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Arxiv |
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We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. |
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HUPBA |
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no |
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Admin @ si @ HPF2024 |
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4021 |
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Jose Luis Gomez; Manuel Silva; Antonio Seoane; Agnes Borras; Mario Noriega; German Ros; Jose Antonio Iglesias; Antonio Lopez |
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All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes |
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Miscellaneous |
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2023 |
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Arxiv |
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We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (this http URL). |
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ADAS |
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no |
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Admin @ si @ GSS2023 |
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4015 |
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Author |
Chenshen Wu; Joost Van de Weijer |
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Title |
Density Map Distillation for Incremental Object Counting |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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2505-2514 |
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We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. |
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Vancouver; Canada; June 2023 |
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CVPRW |
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LAMP |
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no |
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Admin @ si @ WuW2023 |
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3916 |
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Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
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A picture is worth a thousand words but how to organize thousands of pictures? |
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Miscellaneous |
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2018 |
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Arxiv |
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We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization. |
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MILAB; no proj |
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Admin @ si @ LRD2018 |
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3111 |
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Stefan Lonn; Petia Radeva; Mariella Dimiccoli |
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Smartphone picture organization: A hierarchical approach |
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Journal Article |
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2019 |
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Computer Vision and Image Understanding |
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CVIU |
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187 |
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102789 |
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We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization. |
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MILAB; no proj |
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no |
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Admin @ si @ LRD2019 |
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3297 |
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Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
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BoVDW: Bag-of-Visual-and-Depth-Words for Gesture Recognition |
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2012 |
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21st International Conference on Pattern Recognition |
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We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model. |
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1051-4651 |
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978-1-4673-2216-4 |
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ICPR |
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HuPBA;MV |
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Admin @ si @ HBP2012 |
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2122 |
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Author |
Mariella Dimiccoli |
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Figure-ground segregation: A fully nonlocal approach |
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Journal Article |
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2016 |
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Vision Research |
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VR |
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126 |
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308-317 |
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Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion |
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We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas. |
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MILAB; |
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Admin @ si @ Dim2016b |
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2623 |
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Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Conditional Random Fields for image segmentation in Intravascular Ultrasound |
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2010 |
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Medical Image Computing in Catalunya: Graduate Student Workshop |
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13–14 |
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We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved. |
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Girona |
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MICCAT |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ CPF2010 |
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1453 |
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