11 citations · 19 across the 14 of their papers we have counts for
14 papers
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Saehyung Lee, Seunghyun Yoon, Trung Bui +2
Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection m…
Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens
Jaihyun Lew, Soohyuk Jang, Jaehoon Lee +6
Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success…
Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection
Saehyung Lee, Jisoo Mok, Sangha Park +3
In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully par…
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context
Sangwon Yu, Ik-hwan Kim, Jongyoon Song +3
Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often…
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach
Saehyung Lee, Sangwon Yu, Junsung Park +2
In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the gen…
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
Jonghyun Lee, Dahuin Jung, Saehyung Lee +4
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online…
STAG: Structural Test-time Alignment of Gradients for Online Adaptation
Juhyeon Shin, Yujin Oh, Jonghyun Lee +5
Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose Structura…
DAFA: Distance-Aware Fair Adversarial Training
Hyungyu Lee, Saehyung Lee, Hyemi Jang +3
The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aim…
On mitigating stability-plasticity dilemma in CLIP-guided image morphing via geodesic distillation loss
Yeongtak Oh, Saehyung Lee, Uiwon Hwang +1
Large-scale language-vision pre-training models, such as CLIP, have achieved remarkable text-guided image morphing results by leveraging several unconditional generative models. Ho…
On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
Sangha Park, Jisoo Mok, Dahuin Jung +2
Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection…
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training
Saehyung Lee, Hyungyu Lee
Several recent studies have shown that the use of extra in-distribution data can lead to a high level of adversarial robustness. However, there is no guarantee that it will always…
Dataset Condensation with Contrastive Signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung +2
Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-the-art performance when applied to data…
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Saehyung Lee, Changhwa Park, Hyungyu Lee +3
Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have c…
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
Saehyung Lee, Hyungyu Lee, Sungroh Yoon
Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against ad…