103 citations · 309 across the 21 of their papers we have counts for
9 papers · 1 filter
Towards Generalization Across Depth for Monocular 3D Object Detection
Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi +2
While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind. This work advanc…
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Andrea Pilzer, Stéphane Lathuilière, Dan Xu +3
Recent deep monocular depth estimation approaches based on supervised regression have achieved remarkable performance. However, they require costly ground truth annotations during…
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci +2
While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitab…
Budget-Aware Adapters for Multi-Domain Learning
Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi +4
Multi-Domain Learning (MDL) refers to the problem of learning a set of models derived from a common deep architecture, each one specialized to perform a task in a certain domain (e…
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
Andrea Pilzer, Stéphane Lathuilière, Nicu Sebe +1
Nowadays, the majority of state of the art monocular depth estimation techniques are based on supervised deep learning models. However, collecting RGB images with associated depth…
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Zhenyu Zhang, Stéphane Lathuilière, Andrea Pilzer +3
In this work, we tackle the problem of online adaptation for stereo depth estimation, that consists in continuously adapting a deep network to a target video recordedin an environm…