3 citations · 4 across the 7 of their papers we have counts for
7 papers
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis
Kyeongryeol Go
The performance of deep neural networks is strongly influenced by the quality of their training data. However, mitigating dataset bias by manually curating challenging edge cases r…
Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective
Seunghyeon Kim, Kyeongryeol Go
Fisheye cameras introduce significant distortion and pose unique challenges to object detection models trained on conventional datasets. In this work, we propose a data-centric pip…
ZERO: Industry-ready Vision Foundation Model with Multi-modal Prompts
Sangbum Choi, Kyeongryeol Go, Taewoong Jang
Foundation models have revolutionized AI, yet they struggle with zero-shot deployment in real-world industrial settings due to a lack of high-quality, domain-specific datasets. To…
Transferable Candidate Proposal with Bounded Uncertainty
Kyeongryeol Go, Kye-Hyeon Kim
From an empirical perspective, the subset chosen through active learning cannot guarantee an advantage over random sampling when transferred to another model. While it underscores…
Neural Processes with Stochastic Attention: Paying more attention to the context dataset
Mingyu Kim, Kyeongryeol Go, Se-Young Yun
Neural processes (NPs) aim to stochastically complete unseen data points based on a given context dataset. NPs essentially leverage a given dataset as a context representation to d…
Meta-learning Amidst Heterogeneity and Ambiguity
Kyeongryeol Go, Seyoung Yun
Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tas…