collaborators

5 papers

cs.CR2026

On Seeding Watermarks to Detect Verbatim LLM Copy-Paste Responses

Aizierjiang Aiersilan, Artin Yousefi, Robert Pless

Large language models (LLMs) have made fluent essay writing, code drafting, and quiz answering instantly available to students at every level, from secondary school through graduat…

cs.CV2026

Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries

Kevin Robbins, Xiaotong Liu, Yu Wu +4

Vision-Language Models like CLIP create aligned embedding spaces for text and images, making it possible for anyone to build a visual classifier by simply naming the classes they w…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…