1 citations · 3 across the 15 of their papers we have counts for
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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…