2 papers
cs.CV2025
Towards Integrating Uncertainty for Domain-Agnostic Segmentation
Jesse Brouwers, Xiaoyan Xing, Alexander Timans
Foundation models for segmentation such as the Segment Anything Model (SAM) family exhibit strong zero-shot performance, but remain vulnerable in shifted or limited-knowledge domai…
cs.IR2025
InPars+: Supercharging Synthetic Data Generation for Information Retrieval Systems
Matey Krastev, Miklos Hamar, Danilo Toapanta +2
This work revisits and extends synthetic query generation pipelines for Neural Information Retrieval (NIR) by leveraging the InPars Toolkit, a reproducible, end-to-end framework fo…