1 citations · 3 across the 15 of their papers we have counts for
19 papers
Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations
Jungwon Park, Changin Choi, Jimyeong Kim +2
As large language models are increasingly deployed across diverse downstream tasks, efficient task adaptation has emerged as a central challenge. In response, a wide range of task…
Beyond On-Policy Exploration: Integrating External Policy Rollouts for Reinforcement Learning in Diffusion Language Models
Wonseok Lee, Jimyeong Kim, Jungmin Ko +1
Recent reinforcement learning methods for diffusion large language models (dLLMs) commonly rely on on-policy rollouts generated by the target dLLM itself. When successful on-policy…
Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals
Jangwon Suh, Soonil Kwon, Jungmin Ko +4
Background and Objective: Continuous wearable electrocardiogram (ECG) monitoring is increasingly used for ambulatory arrhythmia surveillance, yet forecasting impending atrial fibri…
Orthogonal Negative Guidance in Attention Feature Space for Text-to-Image Generation
Jungmin Ko, Jungwon Park, Jimyeong Kim +3
Text-to-image (T2I) models have become increasingly capable of generating high-quality images. Yet, enforcing the explicit absence of a specified object or attribute remains a fund…
When Confidence Misleads: Suffix Anchoring and Anchor-Proximity Confidence Modulation for Diffusion Language Models
Jungwon Park, Jimyeong Kim, Jungmin Ko +2
Diffusion language models generate text by iteratively selecting and denoising masked positions, making position selection a central inference-time decision. Most training-free met…
Selective Aggregation of Attention Maps Improves Diffusion-Based Visual Interpretation
Jungwon Park, Jungmin Ko, Dongnam Byun +1
Numerous studies on text-to-image (T2I) generative models have utilized cross-attention maps to boost application performance and interpret model behavior. However, the distinct ch…
DOS: Directional Object Separation in Text Embeddings for Multi-Object Image Generation
Dongnam Byun, Jungwon Park, Jungmin Ko +2
Recent progress in text-to-image (T2I) generative models has led to significant improvements in generating high-quality images aligned with text prompts. However, these models stil…
Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
Changin Choi, Wonseok Lee, Jungmin Ko +1
Knowledge-intensive visual question answering (VQA) requires external knowledge beyond image content, demanding precise visual grounding and coherent integration of visual and text…
ReFlex: Text-Guided Editing of Real Images in Rectified Flow via Mid-Step Feature Extraction and Attention Adaptation
Jimyeong Kim, Jungwon Park, Yeji Song +2
Rectified Flow text-to-image models surpass diffusion models in image quality and text alignment, but adapting ReFlow for real-image editing remains challenging. We propose a new r…
Soft Head Selection for Injecting ICL-Derived Task Embeddings
Jungwon Park, Jimyeong Kim, Changin Choi +1
Large language models (LLMs) are commonly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT) or in-context learning (ICL). Recently, ICL-driven embedding-base…
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning
Suhyun Kang, Jungwon Park, Wonseok Lee +1
Cross-Domain Few-Shot Learning~(CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches h…
Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models
Jungwon Park, Jungmin Ko, Dongnam Byun +2
Recent text-to-image diffusion models leverage cross-attention layers, which have been effectively utilized to enhance a range of visual generative tasks. However, our understandin…
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning
Choi Changin, Lim Sungjun, Rhee Wonjong
Retrieval-augmented generation can improve audio captioning by incorporating relevant audio-text pairs from a knowledge base. Existing methods typically rely solely on the input au…
Towards a Better Evaluation of Out-of-Domain Generalization
Duhun Hwang, Suhyun Kang, Moonjung Eo +2
The objective of Domain Generalization (DG) is to devise algorithms and models capable of achieving high performance on previously unseen test distributions. In the pursuit of this…
Unveiling Key Aspects of Fine-Tuning in Sentence Embeddings: A Representation Rank Analysis
Euna Jung, Jaeill Kim, Jungmin Ko +2
The latest advancements in unsupervised learning of sentence embeddings predominantly involve employing contrastive learning-based (CL-based) fine-tuning over pre-trained language…
An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM
Wonkyun Kim, Changin Choi, Wonseok Lee +1
Stimulated by the sophisticated reasoning capabilities of recent Large Language Models (LLMs), a variety of strategies for bridging video modality have been devised. A prominent st…
Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization
Jimyeong Kim, Jungwon Park, Wonjong Rhee
In text-to-image personalization, a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images. We initiate our stud…
Harmonizing Visual and Textual Embeddings for Zero-Shot Text-to-Image Customization
Yeji Song, Jimyeong Kim, Wonhark Park +3
In a surge of text-to-image (T2I) models and their customization methods that generate new images of a user-provided subject, current works focus on alleviating the costs incurred…
On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations
Euna Jung, Jaekeol Choi, EungGu Yun +1
Understanding the encoded representation of Deep Neural Networks (DNNs) has been a fundamental yet challenging objective. In this work, we focus on two possible directions for anal…