65 citations · 233 across the 23 of their papers we have counts for
8 papers · 1 filter
Explainable Semantic Mapping for First Responders
Jean Oh, Martial Hebert, Hae-Gon Jeon +3
One of the key challenges in the semantic mapping problem in postdisaster environments is how to analyze a large amount of data efficiently with minimal supervision. To address thi…
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Yu-Xiong Wang, Deva Ramanan, Martial Hebert
CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is t…
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
Kashyap Chitta, Jose M. Alvarez, Martial Hebert
Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions. In this paper, we pre…
Edge-Direct Visual Odometry
Kevin Christensen, Martial Hebert
In this paper we propose an edge-direct visual odometry algorithm that efficiently utilizes edge pixels to find the relative pose that minimizes the photometric error between image…
Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency
Tejas Khot, Shubham Agrawal, Shubham Tulsiani +3
We present a learning based approach for multi-view stereopsis (MVS). While current deep MVS methods achieve impressive results, they crucially rely on ground-truth 3D training dat…
Image Deformation Meta-Networks for One-Shot Learning
Zitian Chen, Yanwei Fu, Yu-Xiong Wang +3
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information. Mimicking the same behavior and synthesizing deformed in…