4 citations · 5 across the 12 of their papers we have counts for
15 papers
Isotropic Embedding Perturbations for Robust Vision Language Encoders
Hyesong Choi, Daeun Kim, Song Park +5
Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer stro…
FluoCLIP: Stain-Aware Focus Quality Assessment in Fluorescence Microscopy
Hyejin Park, Jiwon Yoon, Sumin Park +5
Accurate focus quality assessment (FQA) in fluorescence microscopy is challenging due to stain-dependent optical variations that induce heterogeneous focus behavior across images.…
Enhancing Alignment for Unified Multimodal Models via Semantically-Grounded Supervision
Jiyeong Kim, Yerim So, Hyesong Choi +2
Unified Multimodal Models (UMMs) have emerged as a promising paradigm that integrates multimodal understanding and generation within a unified modeling framework. However, current…
RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
Jueun Ko, Hyewon Park, Hyesong Choi +1
Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense…
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning
Seungmin Baek, Soyul Lee, Hayeon Jo +2
Transfer learning paradigm has driven substantial advancements in various vision tasks. However, as state-of-the-art models continue to grow, classical full fine-tuning often becom…
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models
Hyesong Choi, Daeun Kim, Sungmin Cha +2
In this work, we dive deep into the impact of additive noise in pre-training deep networks. While various methods have attempted to use additive noise inspired by the success of la…
MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling
Jihye Ahn, Hyesong Choi, Soomin Kim +1
In stereo matching, CNNs have traditionally served as the predominant architectures. Although Transformer-based stereo models have been studied recently, their performance still la…
UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matching
Soomin Kim, Hyesong Choi, Jihye Ahn +1
Unlike other vision tasks where Transformer-based approaches are becoming increasingly common, stereo depth estimation is still dominated by convolution-based approaches. This is m…
SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction
Sumin Son, Hyesong Choi, Dongbo Min
Masked Image Modeling (MIM) techniques have redefined the landscape of computer vision, enabling pre-trained models to achieve exceptional performance across a broad spectrum of ta…
Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge
Hyejin Park, Dongbo Min
In the realm of Adversarial Distillation (AD), strategic and precise knowledge transfer from an adversarially robust teacher model to a less robust student model is paramount. Our…
Hybrid-TTA: Continual Test-time Adaptation via Dynamic Domain Shift Detection
Hyewon Park, Hyejin Park, Jueun Ko +1
Continual Test Time Adaptation (CTTA) has emerged as a critical approach for bridging the domain gap between the controlled training environments and the real-world scenarios, enha…
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation
Hayeon Jo, Hyesong Choi, Minhee Cho +1
Transfer learning based on full fine-tuning (FFT) of the pre-trained encoder and task-specific decoder becomes increasingly complex as deep models grow exponentially. Parameter eff…
Collaborative Learning for Enhanced Unsupervised Domain Adaptation
Minhee Cho, Hyesong Choi, Hayeon Jo +1
Unsupervised Domain Adaptation (UDA) endeavors to bridge the gap between a model trained on a labeled source domain and its deployment in an unlabeled target domain. However, curre…
Emerging Property of Masked Token for Effective Pre-training
Hyesong Choi, Hunsang Lee, Seyoung Joung +3
Driven by the success of Masked Language Modeling (MLM), the realm of self-supervised learning for computer vision has been invigorated by the central role of Masked Image Modeling…
Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training
Hyesong Choi, Hyejin Park, Kwang Moo Yi +2
In this paper, we introduce Saliency-Based Adaptive Masking (SBAM), a novel and cost-effective approach that significantly enhances the pre-training performance of Masked Image Mod…