#zero-shot learning
18 papers match
Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation
Jinghong Liu, Yuchuan Deng, Fanping Liu +2
The paper presents FAME, a unified benchmark for evaluating few-shot medical image segmentation methods across multiple anatomical sites, imaging modalities, and settings, and anal…
SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer
Xiaoxiang Dong, William Baron, Hongyi Chen +3
The paper presents SemAnCorr, a training‑free method that uses semantically consistent anchor regions and functional maps to create dense, geometrically coherent correspondences be…
Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
Yitong Shen, Cheng Guo, Peiliang Wang +5
Zero-Fi introduces a contrastive learning framework that aligns Wi‑Fi signal features with natural‑language descriptions of activities, enabling recognition of unseen human activit…
Zero-Shot Face-to-Speech Synthesis via Latent Space Adaptation of a Style-Diffusion TTS Model
Carlos Muñoz-Romero, Jose A. Gonzalez-Lopez
The paper presents a zero-shot Face-to-Speech system that generates a plausible voice from a single facial image by adapting a frozen StyleTTS 2 model with a lightweight face adapt…
Selecting Open-Weight Language Models for Zero-Shot Intent Classification: A Systematic Evaluation of 41 Models
Parishruthi Ganesh, Gerry Dozier, Cheryl Seals
The paper systematically evaluates 41 open-weight language models for zero‑shot intent classification across multiple datasets, analyzing accuracy, calibration, robustness, and dep…
SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment
Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera +2
The paper introduces SUFLECA, a weakly‑supervised framework that learns geometry‑aware features from large image collections to align CAD models to single RGB images, enabling zero…