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Found 7 papers, 3 papers with code
Calibrating Panoramic Depth Estimation for Practical Localization and Mapping
While panoramic images can easily capture the surrounding context from commodity devices, the estimated depth shares the limitations of conventional image-based depth estimation; the performance deteriorates under large domain shifts and the absolute values are still ambiguous to infer from 2D observations.
MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents
We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision.
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in Radiology
Recent advances in vision-language models sheds a light on the long-standing problems of the oversight AI by the understanding both visual and textual concepts and their semantic correspondences.
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data, contaminated with complex mixture of noise.
Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN
In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes.
Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency
We propose a light-weight, self-supervised adaptation for a visual navigation agent to generalize to unseen environment.
SGoLAM: Simultaneous Goal Localization and Mapping for Multi-Object Goal Navigation
We present SGoLAM, short for simultaneous goal localization and mapping, which is a simple and efficient algorithm for Multi-Object Goal navigation.
