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Found 12 papers, 3 papers with code
Bidirectional Captioning for Clinically Accurate and Interpretable Models
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks.
MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosystems by leveraging the vast amount of remote sensing data that is continuously being collected.
RadTex: Learning Efficient Radiograph Representations from Text Reports
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
SAR-to-EO Image Translation with Multi-Conditional Adversarial Networks
This paper explores the use of multi-conditional adversarial networks for SAR-to-EO image translation.
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge
The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022) will be the first competition aimed at the monitoring and analysis of deforestation in the Amazon rainforest at any time and in any weather conditions.
Multimodal Representation Learning via Maximization of Local Mutual Information
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
Adversarial Learning of Semantic Relevance in Text to Image Synthesis
Instead of selecting random training examples, we perform negative sampling based on the semantic distance from a positive example in the class.
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression.
Adversarial nets with perceptual losses for text-to-image synthesis
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text.
Multimodal Sparse Coding for Event Detection
Unsupervised feature learning methods have proven effective for classification tasks based on a single modality.
Multimodal sparse representation learning and applications
In this paper, we present a multimodal framework for learning sparse representations that can capture semantic correlation between modalities.
