204 citations · 213 across the 25 of their papers we have counts for
11 papers · 1 filter
When normalization hallucinates: unseen risks in AI-powered whole slide image processing
Karel Moens, Matthew B. Blaschko, Tinne Tuytelaars +3
Whole slide image (WSI) normalization remains a vital preprocessing step in computational pathology. Increasingly driven by deep learning, these models learn to approximate data di…
CLASH: A Benchmark for Cross-Modal Contradiction Detection
Teodora Popordanoska, Jiameng Li, Matthew B. Blaschko
Contradictory multimodal inputs are common in real-world settings, yet existing benchmarks typically assume input consistency and fail to evaluate cross-modal contradiction detecti…
SoftCFG: Uncertainty-guided Stable Guidance for Visual Autoregressive Model
Dongli Xu, Aleksei Tiulpin, Matthew B. Blaschko
Autoregressive (AR) models have emerged as powerful tools for image generation by modeling images as sequences of discrete tokens. While Classifier-Free Guidance (CFG) has been ado…
Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning
Chang Tian, Matthew B. Blaschko, Mingzhe Xing +3
Reinforcement learning (RL) has become a key technique for enhancing the reasoning abilities of large language models (LLMs), with policy-gradient algorithms dominating the post-tr…
Bayesian Optimization over Bounded Domains with the Beta Product Kernel
Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko +1
Bayesian optimization with Gaussian processes (GP) is commonly used to optimize black-box functions. The Matérn and the Radial Basis Function (RBF) covariance functions are used fr…
Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles
Zifu Wang, Junyi Zhu, Bo Tang +4
The application of rule-based reinforcement learning (RL) to multimodal large language models (MLLMs) introduces unique challenges and potential deviations from findings in text-on…
Revisiting Reweighted Risk for Calibration: AURC, Focal, and Inverse Focal Loss
Han Zhou, Sebastian G. Gruber, Teodora Popordanoska +1
Several variants of reweighted risk functionals, such as focal loss, inverse focal loss, and the Area Under the Risk Coverage Curve (AURC), have been proposed for improving model c…
CARE: Confidence-aware Ratio Estimation for Medical Biomarkers
Jiameng Li, Teodora Popordanoska, Aleksei Tiulpin +3
Ratio-based biomarkers (RBBs), such as the proportion of necrotic tissue within a tumor, are widely used in clinical practice to support diagnosis, prognosis, and treatment plannin…
Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities
Marco Mezzina, Pieter De Backer, Tom Vercauteren +3
Purpose: Automated Surgical Phase Recognition (SPR) uses Artificial Intelligence (AI) to segment the surgical workflow into its key events, functioning as a building block for effi…
DAVE: Diagnostic benchmark for Audio Visual Evaluation
Gorjan Radevski, Teodora Popordanoska, Matthew B. Blaschko +1
Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-…
Using Causality for Enhanced Prediction of Web Traffic Time Series
Chang Tian, Mingzhe Xing, Zenglin Shi +3
Predicting web service traffic has significant social value, as it can be applied to various practical scenarios, including but not limited to dynamic resource scaling, load balanc…