Eric Xing

I am a PhD student in the Multimodal Vision Research Lab at Washington University in St. Louis, where I work on multimodal learing and retrieval. I am advised by Nathan Jacobs.

I received my B.S. in Computer Science with a minor in mathematics from Western Kentucky University. I also worked with Dongwon Lee as part of an REU at Penn State University.

Email / CV / LinkedIn / Scholar / GitHub

Research

My research broadly lies in vision-language models and multimodal learning. I am currently working on various flavors of multimodal retrieval.

	QuARI: Query Adaptive Retrieval Improvement Eric Xing, Abby Stylianou, Robert Pless, Nathan Jacobs arXiv, 2025 arXiv / code Hypernetwork-based framework for dynamic database feature adapation, for retrieval and large-scale reranking.
	ConText-CIR: Learning from Concept in Text for Composed Image Retrieval Eric Xing, Pranavi Kolouju, Robert Pless, Abby Stylianou, Nathan Jacobs IEEE Conference on Computer Vision and Pattern Recognition (CVPR'25), 2025 arXiv / code State-of-the-art composed image retrieval by bootstrapping learning from concept representations.
	RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings Aayush Dhakal, Srikumar Sastry, Subash Khanal, Adeel Ahmad, Eric Xing, Nathan Jacobs IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025 arXiv / code We develop a retrieval-augmented strategy for geospatial representation learning, significantly improving performance on downstream tasks.
	good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval Pranavi Kolouju, Eric Xing, Robert Pless, Nathan Jacobs, Abby Stylianou 2nd Workshop on Harnessing Generative Models for Synthetic Visual Datasets at CVPR, 2025 arXiv / code Dataset synthesis pipeline to for training of powerful composed image retrieval models.
	GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching Feng Qiao, Zhexiao Xiong, Eric Xing, Nathan Jacobs arXiv, 2025 website / arXiv / code GenStereo uses a diffusion-based approach with disparity-aware conditioning for strong performance in stereo image generation and unsupervised stereo matching.
	PSM: Learning Probabilistic Embeddings for Multi-scale Zero-Shot Soundscape Mapping Subash Khanal, Eric Xing, Srikumar Sastry, Aayush Dhakal, Zhexiao Xiong, Adeel Ahmad, Nathan Jacobs ACM Multimedia, 2024 paper / arXiv / code / bibtex We develop a probabilistic, multi-scale, and metadata-aware embedding space that connects audio, text, and overhead imagery.
	ALISON: Fast and Effective Stylometric Authorship Obfuscation Eric Xing, Saranya Venkatraman, Thai Le, Dongwon Lee AAAI Conference on Artificial Intelligence (AAAI), 2024 paper / arXiv / code / bibtex An authorship obfuscation method that demonstrates a ~10x speedup over previous methods while outperforming them in terms of attack success, semantic preservation, and fluency.
	Neural Network Decision-Making Criteria Consistency Analysis via Inputs Sensitivity Eric Xing, Liangliang Liu, Xin Xing, Yunni Qu, Nathan Jacobs, Gongbo Liang International Conference on Pattern Recognition (ICPR), 2022 paper / bibtex Quantification and analysis of neural network decision-making criteria inconsistency and three algorithms to mitigate this inconsistency with minimal performance sacrifice.
	Beware the Black-Box of Medical Image Generation: an Uncertainty Analysis by the Learned Feature Space Yunni Qu, David Yan, Eric Xing, Fengbo Zheng, Jie Zhang, Liangliang Liu, Gongbo Liang International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022 paper / bibtex Quantitative and clustering-based analyses of learned features spaces of U-Net architechtures for medical image generation.
	A Toolkit for Assessments in Introductory Programming Courses Eric Xing, Guangming Xing ACM Technical Symposium on Computer Science Education (SIGCSE), 2022 paper / bibtex A versatile online exam toolkit with plagarism and cheating detection as part of the vLab learning management system.
	Motorcycle Safety Investigation in Kentucky Using Machine and Deep Learning Techniques Eric Xing, Kirolos Haleem International Conference on Transportation and Development, 2022 paper / bibtex Interpretability over a new state-of-the-art pipeline for motorcycle crash severity predicition for the analysis of factors contributing to severe motorcycle crashes.

Thank you to Jon Barron for the site template.