13 citations · 45 across the 13 of their papers we have counts for
13 papers
Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge
Hongruixuan Chen, He Huang, Haifeng Wang +19
Rapid post-disaster response requires timely, building-level information on whether structures remain intact, are damaged, or are destroyed. Post-event optical imagery, however, ma…
Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing
Madeline Anderson, Miriam Cha, William T. Freeman +3
Vision language models have achieved impressive results across various fields. However, adoption in remote sensing remains limited, largely due to the scarcity of paired image-text…
Improving Medical Visual Representations via Radiology Report Generation
Keegan Quigley, Miriam Cha, Josh Barua +4
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks. Contrastive learning approaches h…
MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge
Miriam Cha, Gregory Angelides, Mark Hamilton +6
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 ecosyste…
RadTex: Learning Efficient Radiograph Representations from Text Reports
Keegan Quigley, Miriam Cha, Ruizhi Liao +4
Automated analysis of chest radiography using deep learning has tremendous potential to enhance the clinical diagnosis of diseases in patients. However, deep learning models typica…
SAR-to-EO Image Translation with Multi-Conditional Adversarial Networks
Armando Cabrera, Miriam Cha, Prafull Sharma +1
This paper explores the use of multi-conditional adversarial networks for SAR-to-EO image translation. Previous methods condition adversarial networks only on the input SAR. We sho…
Developing a Series of AI Challenges for the United States Department of the Air Force
Vijay Gadepally, Gregory Angelides, Andrei Barbu +39
Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have…
Multimodal Representation Learning via Maximization of Local Mutual Information
Ruizhi Liao, Daniel Moyer, Miriam Cha +5
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn…
Adversarial Learning of Semantic Relevance in Text to Image Synthesis
Miriam Cha, Youngjune L. Gwon, H. T. Kung
We describe a new approach that improves the training of generative adversarial nets (GANs) for synthesizing diverse images from a text input. Our approach is based on the conditio…
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
Miriam Cha, Youngjune Gwon, H. T. Kung
We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embedding…
Adversarial nets with perceptual losses for text-to-image synthesis
Miriam Cha, Youngjune Gwon, H. T. Kung
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated ima…
Multimodal Sparse Coding for Event Detection
Youngjune Gwon, William Campbell, Kevin Brady +3
Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representa…
Multimodal sparse representation learning and applications
Miriam Cha, Youngjune Gwon, H. T. Kung
Unsupervised methods have proven effective for discriminative tasks in a single-modality scenario. In this paper, we present a multimodal framework for learning sparse representati…