1 citations · 1 across the 3 of their papers we have counts for
6 papers
Global and Local Entailment Learning for Natural World Imagery
Srikumar Sastry, Aayush Dhakal, Eric Xing +2
Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment le…
QuARI: Query Adaptive Retrieval Improvement
Eric Xing, Abby Stylianou, Robert Pless +1
Massive-scale pretraining has made vision-language models increasingly popular for image-to-image and text-to-image retrieval across a broad collection of domains. However, these m…
ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
Eric Xing, Pranavi Kolouju, Robert Pless +2
Composed image retrieval (CIR) is the task of retrieving a target image specified by a query image and a relative text that describes a semantic modification to the query image. Ex…
RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings
Aayush Dhakal, Srikumar Sastry, Subash Khanal +3
The choice of representation for geographic location significantly impacts the accuracy of models for a broad range of geospatial tasks, including fine-grained species classificati…
good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval
Pranavi Kolouju, Eric Xing, Robert Pless +2
Composed image retrieval (CIR) enables users to search images using a reference image combined with textual modifications. Recent advances in vision-language models have improved C…
Towards Open-World Generation of Stereo Images and Unsupervised Matching
Feng Qiao, Zhexiao Xiong, Eric Xing +1
Stereo images are fundamental to numerous applications, including extended reality (XR) devices, autonomous driving, and robotics. Unfortunately, acquiring high-quality stereo imag…