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From the 1 of 32 papers with an AI index.

most citedBias in Large Language Models: Origin, Evaluation, and Mitigation

24 citations

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6 papers · 1 filter

cs.CV2026

Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

Abhijeet Parida, Antonia Alomar, Zhifan Jiang +7

Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-spe…

cs.CV20261 cited

Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

Abhijeet Parida, Daniel Capellán-Martín, Zhifan Jiang +6

Gliomas are the most common malignant brain tumors in adults and are among the most lethal. Despite aggressive treatment, the median survival rate is less than 15 months. Accurate…

cs.CV2026

Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble

Daniel Capellán-Martín, Abhijeet Parida, Zhifan Jiang +6

Robust and generalizable segmentation of brain tumors on multi-parametric magnetic resonance imaging (MRI) remains difficult because tumor types differ widely. The BraTS 2025 Light…

cs.CV20264 cited

Deep Transformer Network for Monocular Pose Estimation of Shipborne Unmanned Aerial Vehicle

Maneesha Wickramasuriya, Taeyoung Lee, Murray Snyder

This paper introduces a deep transformer network for estimating the relative 6D pose of a Unmanned Aerial Vehicle (UAV) with respect to a ship using monocular images. A synthetic d…

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.CV20263 cited

PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning

Kexin Baoa, Fanzhao Lin, Zichen Wang +3

Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting a…