6 papers
Stress Testing Concept Erasure with Large Language Model Agents
Yuyang Xue, Feng Chen, Zhihua Liu +4
Concept erasure aims to remove semantic concepts from a trained generative model and is increasingly important for responsible AI deployment. However, verifying whether a model has…
CSEval: A Framework for Evaluating Clinical Semantics in Text-to-Image Generation
Robert Cronshaw, Konstantinos Vilouras, Junyu Yan +4
Text-to-image generation has been increasingly applied in medical domains for various purposes such as data augmentation and education. Evaluating the quality and clinical reliabil…
SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation
Junyu Yan, Feng Chen, Yuyang Xue +4
Recent studies have shown that Machine Learning (ML) models can exhibit bias in real-world scenarios, posing significant challenges in ethically sensitive domains such as healthcar…
Count2Density: Crowd Density Estimation without Location-level Annotations
Mattia Litrico, Feng Chen, Michael Pound +3
Crowd density estimation is a well-known computer vision task aimed at estimating the density distribution of people in an image. The main challenge in this domain is the reliance…
CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models
Yuyang Xue, Edward Moroshko, Feng Chen +3
Text-to-Image diffusion models can produce undesirable content that necessitates concept erasure. However, existing methods struggle with under-erasure, leaving residual traces of…
GMT: Guided Mask Transformer for Leaf Instance Segmentation
Feng Chen, Sotirios A. Tsaftaris, Mario Valerio Giuffrida
Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. Accurate segmentation of each leaf…