722 citations · 1.1k across the 17 of their papers we have counts for
21 papers
3D molecule generation by denoising voxel grids
Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz +6
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from…
Touch-based Curiosity for Sparse-Reward Tasks
Sai Rajeswar, Cyril Ibrahim, Nitin Surya +4
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. In this wo…
Unsupervised Learning of Dense Visual Representations
Pedro O. Pinheiro, Amjad Almahairi, Ryan Y. Benmalek +2
Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these methods learn global (image-level) repres…
Reinforced active learning for image segmentation
Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh +1
Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-consuming. Second, realistic segmentation…
Neural Multisensory Scene Inference
Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh +2
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 look…
Instance Segmentation with Point Supervision
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2
Instance segmentation methods often require costly per-pixel labels. We propose a method that only requires point-level annotations. During training, the model only has access to a…
CLAREL: Classification via retrieval loss for zero-shot learning
Boris N. Oreshkin, Negar Rostamzadeh, Pedro O. Pinheiro +1
We address the problem of learning fine-grained cross-modal representations. We propose an instance-based deep metric learning approach in joint visual and textual space. The key n…
Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks
Kris Y. Hong, Pedro O. Pinheiro, Scott Weichenthal
Here we present a new method of estimating global variations in outdoor PM concentrations using satellite images combined with ground-level measurements and deep convolutio…
Reinforced Imitation in Heterogeneous Action Space
Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio +2
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an…
Adaptive Cross-Modal Few-Shot Learning
Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin +1
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance m…
Adversarial Framing for Image and Video Classification
Konrad Zolna, Michal Zajac, Negar Rostamzadeh +1
Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly modifying most of its pixels, or by occluding it…
Domain-Adaptive Single-View 3D Reconstruction
Pedro O. Pinheiro, Negar Rostamzadeh, Sungjin Ahn
Single-view 3D shape reconstruction is an important but challenging problem, mainly for two reasons. First, as shape annotation is very expensive to acquire, current methods rely o…
Where are the Blobs: Counting by Localization with Point Supervision
Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro +2
Object counting is an important task in computer vision due to its growing demand in applications such as surveillance, traffic monitoring, and counting everyday objects. State-of-…
Unsupervised Domain Adaptation with Similarity Learning
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 diffe…
A MultiPath Network for Object Detection
Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin +4
The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical imag…
Learning to Refine Object Segments
Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert +1
Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture…
Learning to Segment Object Candidates
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 p…
Phrase-based Image Captioning
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. In this paper, we present a simple model…
Simple Image Description Generator via a Linear Phrase-Based Approach
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. In this paper, we present a simple model…
From Image-level to Pixel-level Labeling with Convolutional Networks
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. This problem…
Recurrent Convolutional Neural Networks for Scene Parsing
Pedro H. O. Pinheiro, Ronan Collobert
Scene parsing is a technique that consist on giving a label to all pixels in an image according to the class they belong to. To ensure a good visual coherence and a high class accu…