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Found 6 papers, 2 papers with code
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models (i. e., foundation models) in research fields such as Natural Language Processing (NLP) and Computer Vision (CV), we devote this survey to exploring (i) how these existing foundation models from NLP and CV can be applied to the field of robotics, and also exploring (ii) what a robotics-specific foundation model would look like.
Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image
However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated.
AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
Few-shot object detection has attracted increasing attention and rapidly progressed in recent years.
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly.
Semi-supervised Learning for COVID-19 Image Classification via ResNet
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community.
Combating the Compounding-Error Problem with a Multi-step Model
In this paper, we address the compounding-error problem by introducing a multi-step model that directly outputs the outcome of executing a sequence of actions.
Model-based Reinforcement Learning
reinforcement-learning
+1
