1 citations · 2 across the 9 of their papers we have counts for
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Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models
Myeongkyun Kang, Yanting Yang, Xiaoxiao Li
Fine-grained visual representations are essential for medical image analysis, particularly when diagnostically relevant evidence is subtle and spatially localized. Modern transform…
UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning
Gexin Huang, Yanting Yang, Myeongkyun Kang +6
Vision-language models (VLMs) excel on visual question answering and multimodal reasoning benchmarks. Yet their capability on ultra-resolution images - where critical evidence is t…
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Ruinan Jin, Beidi Zhao, Myeongkyun Kang +2
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a default safety layer for medical…
Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following
Myeongkyun Kang, Soopil Kim, Xiaoxiao Li +1
Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visual instruction tuning, which fi…
LoFi: Location-Aware Fine-Grained Representation Learning for Chest X-ray
Myeongkyun Kang, Yanting Yang, Xiaoxiao Li
Fine-grained representation learning is crucial for retrieval and phrase grounding in chest X-rays, where clinically relevant findings are often spatially confined. However, the la…
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
Soopil Kim, Sion An, Philip Chikontwe +4
Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately su…
Content Preserving Image Translation with Texture Co-occurrence and Spatial Self-Similarity for Texture Debiasing and Domain Adaptation
Myeongkyun Kang, Dongkyu Won, Miguel Luna +4
Models trained on datasets with texture bias usually perform poorly on out-of-distribution samples since biased representations are embedded into the model. Recently, various image…