Search Results for author:
Found 7 papers, 3 papers with code
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source.
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions.
A recurrent multi-scale approach to RBG-D Object Recognition
Technological development aims to produce generations of increasingly efficient robots able to perform complex tasks.
Multimodal Deep Domain Adaptation
Evaluations have been done using different data types: RGB only, depth only and RGB-D over the following datasets, designed for the robotic community: RGB-D Object Dataset (ROD), Web Object Dataset (WOD), Autonomous Robot Indoor Dataset (ARID), Big Berkeley Instance Recognition Dataset (BigBIRD) and Active Vision Dataset.
Recurrent Convolutional Fusion for RGB-D Object Recognition
Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision.
Reconstruct & Crush Network
This article introduces an energy-based model that is adversarial regarding data: it minimizes the energy for a given data distribution (the positive samples) while maximizing the energy for another given data distribution (the negative or unlabeled samples).
Recognizing Objects In-the-wild: Where Do We Stand?
The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments.
