34 citations · 56 across the 67 of their papers we have counts for
16 papers · 1 filter
Addressing Text Embedding Leakage in Diffusion-based Image Editing
Sunung Mun, Jinhwan Nam, Sunghyun Cho +1
Text-based image editing, powered by generative diffusion models, lets users modify images through natural-language prompts and has dramatically simplified traditional workflows. D…
Combinatorial Rising Bandits
Seockbean Song, Youngsik Yoon, Siwei Wang +2
Combinatorial online learning is a fundamental task for selecting the optimal action (or super arm) as a combination of base arms in sequential interactions with systems providing…
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…
Exploring Iterative Controllable Summarization with Large Language Models
Sangwon Ryu, Heejin Do, Daehee Kim +5
Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., leng…
Comparison-based Active Preference Learning for Multi-dimensional Personalization
Minhyeon Oh, Seungjoon Lee, Jungseul Ok
Large language models (LLMs) have shown remarkable success, but aligning them with human preferences remains a core challenge. As individuals have their own, multi-dimensional pref…
Retrieval-Augmented Generation with Estimation of Source Reliability
Jeongyeon Hwang, Junyoung Park, Hyejin Park +3
Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs) by retrieving information from external databases, whi…
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients
Jabin Koo, Minwoo Jang, Jungseul Ok
Federated fine-tuning for Large Language Models (LLMs) faces significant challenges due to the heavy communication overhead of transmitting large model updates. Although Low Rank A…
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation
Jaechang Kim, Jinmin Goh, Inseok Hwang +2
Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given…
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
Nayeong Kim, Juwon Kang, Sungsoo Ahn +2
We study the problem of training an unbiased and accurate model given a dataset with multiple biases. This problem is challenging since the multiple biases cause multiple undesirab…
Activity-Guided Industrial Anomalous Sound Detection against Interferences
Yunjoo Lee, Jaechang Kim, Jungseul Ok
We address a practical scenario of anomaly detection for industrial sound data, where the sound of a target machine is corrupted by background noise and interference from neighbori…
An Investigation Into Explainable Audio Hate Speech Detection
Jinmyeong An, Wonjun Lee, Yejin Jeon +3
Research on hate speech has predominantly revolved around detection and interpretation from textual inputs, leaving verbal content largely unexplored. While there has been limited…
Key-Element-Informed sLLM Tuning for Document Summarization
Sangwon Ryu, Heejin Do, Yunsu Kim +2
Remarkable advances in large language models (LLMs) have enabled high-quality text summarization. However, this capability is currently accessible only through LLMs of substantial…
Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning
Sangwon Ryu, Heejin Do, Yunsu Kim +2
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a spec…
CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment
Hyeongmin Lee, Kyoungkook Kang, Jungseul Ok +1
Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment. However, these approaches…
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
Hyejin Park, Jeongyeon Hwang, Sunung Mun +2
Test-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA m…
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…