1 citations · 2 across the 25 of their papers we have counts for
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Rare Text Semantics Were Always There in Your Diffusion Transformer
Seil Kang, Woojung Han, Dayun Ju +1
Starting from flow- and diffusion-based transformers, Multi-modal Diffusion Transformers (MM-DiTs) have reshaped text-to-vision generation, gaining acclaim for exceptional visual f…
Interpreting Attention Heads for Image-to-Text Information Flow in Large Vision-Language Models
Jinyeong Kim, Seil Kang, Jiwoo Park +2
Large Vision-Language Models (LVLMs) answer visual questions by transferring information from images to text through a series of attention heads. While this image-to-text informati…
Interpreting vision transformers via residual replacement model
Jinyeong Kim, Junhyeok Kim, Yumin Shim +3
How do vision transformers (ViTs) represent and process the world? This paper addresses this long-standing question through the first systematic analysis of 6.6K features across al…
Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration
Kyobin Choo, Hyunkyung Han, Jinyeong Kim +2
In clinical practice, imaging modalities with functional characteristics, such as positron emission tomography (PET) and fractional anisotropy (FA), are often aligned with a struct…
Backbone Augmented Training for Adaptations
Jae Wan Park, Junhyeok Kim, Youngjun Jun +2
Adaptations facilitate efficient training of large backbone models, including diffusion models for image generation and transformer-based language models. While various adaptation…
WAVE: Warp-Based View Guidance for Consistent Novel View Synthesis Using a Single Image
Jiwoo Park, Tae Eun Choi, Youngjun Jun +1
Generating high-quality novel views of a scene from a single image requires maintaining structural coherence across different views, referred to as view consistency. While diffusio…
PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation Learning
Yeonkyung Lee, Woojung Han, Youngjun Jun +3
Retinal foundation models have significantly advanced retinal image analysis by leveraging self-supervised learning to reduce dependence on labeled data while achieving strong gene…
Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis
Woojung Han, Yeonkyung Lee, Chanyoung Kim +2
Diffusion-based text-to-image (T2I) models have recently excelled in high-quality image generation, particularly in a training-free manner, enabling cost-effective adaptability and…
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding
Seil Kang, Jinyeong Kim, Junhyeok Kim +1
Visual grounding seeks to localize the image region corresponding to a free-form text description. Recently, the strong multimodal capabilities of Large Vision-Language Models (LVL…
See What You Are Told: Visual Attention Sink in Large Multimodal Models
Seil Kang, Jinyeong Kim, Junhyeok Kim +1
Large multimodal models (LMMs) "see" images by leveraging the attention mechanism between text and visual tokens in the transformer decoder. Ideally, these models should focus on k…
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes
Donghyun Kim, Chanyoung Kim, Hyunah Ko +1
While 3D point clouds are widely used in vision applications, their irregular and sparse nature make them challenging to handle. In response, numerous encoding approaches have been…
Pathology-Aware Adaptive Watermarking for Text-Driven Medical Image Synthesis
Chanyoung Kim, Dayun Ju, Jinyeong Kim +3
As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown. This issue is critical in the…