I am a third year PhD student and a graduate research assistant in the College of Information and Computer Sciences at the University of Massachusetts Amherst under the supervision of Subhransu Maji in the Computer Vision Lab. Previously, I spent three years as a graduate research assistant under the supervision of Pablo Arbeláez in the Biomedical Computer Vision Group at the Universidad de Los Andes. My interests are in Pattern Recognition in Computer Vision using Artificial Intelligence.
- 07/07/2021 - Paper accepted at IEEE Transactions on Medical Imaging.
- 11/30/2020 - StarcNet project accepted at The Astrophysical Journal (ApJ).
- 05/20/2020 - Paper accepted at Medical & Biological Engineering & Computing.
- 11/08/2019 - Invited talk about FLC dataset at Camera Trap Tech Symposium @ Google HQ. Talk slides.
- 05/17/2019 - Paper accepted at The Sixth Workshop on Fine-Grained Visual Categorization (FGVC6), CVPR 2019.
- 10/05/2017 - Paper accepted (oral presentation) at SIPAIM2017.
- 09/01/2017 - Colciencias-Fulbright Cohort 2018 scholarship awarded to undertake Ph.D. studies in the U.S.
Finding Four-Leaf Clovers: A Benchmark for Fine-Grained Object Localization
Laura Bravo*, Alejandro Pardo*, Gustavo Perez*, Pablo Arbelaez
The Sixth Workshop on Fine-Grained Visual Categorization (FGVC6), CVPR 2019.
We present the Four-Leaf Clover (FLC) dataset, a new experimental framework for studying fine-grained object localization problems. We built the FLC dataset with the contribution of trained hobbyists, who were assigned the task of spotting four-leaf clovers on a fixed geographical extension over two clover seasons, one season for the train set and another for the test set. We then annotated each object instance for the tasks of object detection, semantic segmentation, instance segmentation, object parsing and semantic boundary detection. Our dataset is composed of more than 100,000 images, containing 2,151 carefully annotated clover instances of four, five or six leaves. The FLC dataset is extremely challenging and adapted to fine-grained object localization problems due to its small inter-class variance and its very large intra-class variation. We perform extensive experiments with state-of-the-art methods in order to establish strong baselines for each of the tasks.