1 citations · 1 across the 11 of their papers we have counts for
12 papers
AdaBoosting Text Prompts for Vision-Language Models
Seokhee Jin, Changhwan Sung, Sunung Mun +2
The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts. Handcrafted templates and Large Language Model (LLM)-generated de…
Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
Jabin Koo, Hoyoung Kim, Minwoo Jang +1
Federated Learning (FL) offers a privacy-preserving pathway for aligning Large Language Models (LLMs); however, existing frameworks typically enforce a monolithic reward model, ine…
MMTB: Evaluating Terminal Agents on Multimedia-File Tasks
Chiyeong Heo, Jaechang Kim, Junhyuk Kwon +4
Terminals provide a powerful interface for AI agents by exposing diverse tools for automating complex workflows, yet existing terminal-agent benchmarks largely focus on tasks groun…
ChimeraLoRA: Multi-Head LoRA-Guided Synthetic Datasets
Hoyoung Kim, Minwoo Jang, Jabin Koo +2
Beyond general recognition tasks, specialized domains and fine-grained settings often encounter data scarcity, especially for tail classes. To obtain less biased and more reliable…
Making Models Unmergeable via Scaling-Sensitive Loss Landscape
Minwoo Jang, Hoyoung Kim, Jabin Koo +1
The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet, this modularity also creates a…
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs
Sungjae Lee, Hoyoung Kim, Jeongyeon Hwang +2
Scaling test-time computation--generating and analyzing multiple or sequential outputs for a single input--has become a promising strategy for improving the reliability and quality…
Enhancing Cost Efficiency in Active Learning with Candidate Set Query
Yeho Gwon, Sehyun Hwang, Hoyoung Kim +2
This paper introduces a cost-efficient active learning (AL) framework for classification, featuring a novel query design called candidate set query. Unlike traditional AL queries r…
Active Prompt Learning with Vision-Language Model Priors
Hoyoung Kim, Seokhee Jin, Changhwan Sung +2
Vision-language models (VLMs) have demonstrated remarkable zero-shot performance across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts for e…
Active Label Correction for Semantic Segmentation with Foundation Models
Hoyoung Kim, Sehyun Hwang, Suha Kwak +1
Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundat…
Active Learning for Semantic Segmentation with Multi-class Label Query
Sehyun Hwang, Sohyun Lee, Hoyoung Kim +3
This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions…
Adaptive Superpixel for Active Learning in Semantic Segmentation
Hoyoung Kim, Minhyeon Oh, Sehyun Hwang +2
Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learn…
Robust Deep Learning from Crowds with Belief Propagation
Hoyoung Kim, Seunghyuk Cho, Dongwoo Kim +1
Crowdsourcing systems enable us to collect large-scale dataset, but inherently suffer from noisy labels of low-paid workers. We address the inference and learning problems using su…