1 code implementation • NeurIPS 2023 • Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Martin Watkins, Stephen Ra, Vishnu Sresht, Saeed Saremi
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids.
no code implementations • 29 Sep 2021 • Ryan Y Benmalek, Sabhya Chhabria, Pedro O. Pinheiro, Claire Cardie, Serge Belongie
These models outperform both previous work and static models under both \emph{static} and \emph{continual} semantic shifts, suggesting that ``learning to adapt'' is a useful capability for models and agents in a changing world.
1 code implementation • 1 Apr 2021 • Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro
Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion.
no code implementations • NeurIPS 2020 • Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek, Florian Golemo, Aaron Courville
VADeR provides a natural representation for dense prediction tasks and transfers well to downstream tasks.
1 code implementation • ICLR 2020 • Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher J. Pal
Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems.
2 code implementations • NeurIPS 2019 • Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn
For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e. g., by looking at and touching objects.
no code implementations • 14 Jun 2019 • Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt
Instance segmentation methods often require costly per-pixel labels.
no code implementations • 1 Jun 2019 • Kris Y. Hong, Pedro O. Pinheiro, Scott Weichenthal
Here we present a new method of estimating global variations in outdoor PM$_{2. 5}$ concentrations using satellite images combined with ground-level measurements and deep convolutional neural networks.
no code implementations • 31 May 2019 • Boris N. Oreshkin, Negar Rostamzadeh, Pedro O. Pinheiro, Christopher Pal
We address the problem of learning fine-grained cross-modal representations.
no code implementations • ICLR 2019 • Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse.
no code implementations • 6 Apr 2019 • Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse.
1 code implementation • NeurIPS 2019 • Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro
Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested.
1 code implementation • 11 Dec 2018 • Konrad Zolna, Michal Zajac, Negar Rostamzadeh, Pedro O. Pinheiro
Neural networks are prone to adversarial attacks.
1 code implementation • ICCV 2019 • Pedro O. Pinheiro, Negar Rostamzadeh, Sungjin Ahn
In this paper, we propose a framework to improve over these challenges using adversarial training.
3 code implementations • ECCV 2018 • Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt
However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods.
Ranked #1 on Object Counting on Pascal VOC 2007 count-test
no code implementations • CVPR 2018 • Pedro O. Pinheiro
The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution.
1 code implementation • 7 Apr 2016 • Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár
To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.
Ranked #104 on Instance Segmentation on COCO test-dev
2 code implementations • 29 Mar 2016 • Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollàr
In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.
Ranked #4 on Region Proposal on COCO test-dev
2 code implementations • NeurIPS 2015 • Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar
Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier.
no code implementations • 12 Feb 2015 • Rémi Lebret, Pedro O. Pinheiro, Ronan Collobert
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.
no code implementations • 29 Dec 2014 • Remi Lebret, Pedro O. Pinheiro, Ronan Collobert
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.
no code implementations • CVPR 2015 • Pedro O. Pinheiro, Ronan Collobert
We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task.