21 citations · 25 across the 10 of their papers we have counts for
10 papers
ReDesign: Recovering Editable Design Structures from Images via Agentic Decomposition
Jooyeol Yun, Jintae Park, Hyesu Lim +3
Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering…
VisualScratchpad: Inference-time Visual Concepts Analysis in Vision Language Models
Hyesu Lim, Jinho Choi, Taekyung Kim +3
High-performing vision language models still produce incorrect answers, yet their failure modes are often difficult to explain. To make model internals more accessible and enable s…
ConceptScope: Characterizing Dataset Bias via Disentangled Visual Concepts
Jinho Choi, Hyesu Lim, Steffen Schneider +1
Dataset bias, where data points are skewed to certain concepts, is ubiquitous in machine learning datasets. Yet, systematically identifying these biases is challenging without cost…
CytoSAE: Interpretable Cell Embeddings for Hematology
Muhammed Furkan Dasdelen, Hyesu Lim, Michele Buck +3
Sparse autoencoders (SAEs) emerged as a promising tool for mechanistic interpretability of transformer-based foundation models. Very recently, SAEs were also adopted for the visual…
Sparse autoencoders reveal selective remapping of visual concepts during adaptation
Hyesu Lim, Jinho Choi, Jaegul Choo +1
Adapting foundation models for specific purposes has become a standard approach to build machine learning systems for downstream applications. Yet, it is an open question which mec…
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering
ChaeHun Park, Koanho Lee, Hyesu Lim +5
Building a reliable visual question answering~(VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To addre…
Towards Calibrated Robust Fine-Tuning of Vision-Language Models
Changdae Oh, Hyesu Lim, Mijoo Kim +6
Improving out-of-distribution (OOD) generalization during in-distribution (ID) adaptation is a primary goal of robust fine-tuning of zero-shot models beyond naive fine-tuning. Howe…
PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration
Minseok Choi, Hyesu Lim, Jaegul Choo
Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document. A key challenge in DocRE is the cost of annotating such data which requires…
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo +1
This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., trans…
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
Jimin Hong, Taehee Kim, Hyesu Lim +1
During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained…