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Found 8 papers, 1 papers with code
On the Hidden Mystery of OCR in Large Multimodal Models
Large models have recently played a dominant role in natural language processing and multimodal vision-language learning.
Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming at locating the oriented objects of numerous predefined object categories.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints
Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Minimal Solutions for Relative Pose with a Single Affine Correspondence
In this paper we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points and we demonstrate efficient solvers for these cases.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
