29 citations · 30 across the 3 of their papers we have counts for
5 papers
Heterogeneous Multi-task Learning with Expert Diversity
Raquel Aoki, Frederick Tung, Gabriel L. Oliveira
Predicting multiple heterogeneous biological and medical targets is a challenge for traditional deep learning models. In contrast to single-task learning, in which a separate model…
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation
Gabriel L. Oliveira, Senthil Yogamani, Wolfram Burgard +1
Single encoder-decoder methodologies for semantic segmentation are reaching their peak in terms of segmentation quality and efficiency per number of layers. To address these limita…
Topometric Localization with Deep Learning
Gabriel L. Oliveira, Noha Radwan, Wolfram Burgard +1
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost…
Deep Semantic Classification for 3D LiDAR Data
Ayush Dewan, Gabriel L. Oliveira, Wolfram Burgard
Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In th…
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
Mohammadreza Zolfaghari, Gabriel L. Oliveira, Nima Sedaghat +1
General human action recognition requires understanding of various visual cues. In this paper, we propose a network architecture that computes and integrates the most important vis…