1 citations · 2 across the 8 of their papers we have counts for
14 papers
TorR: Towards Brain-Inspired Task-Oriented Reasoning via Cache-Oriented Algorithm-Architecture Co-design
Hyunwoo Oh, SungHeon Jeong, Suyeon Jang +4
Task-oriented object detection (TOOD) atop CLIP offers open-vocabulary, prompt-driven semantics, yet dense per-window computation and heavy memory traffic hinder real-time, power-l…
MERIT: Multi-domain Efficient RAW Image Translation
Wenjun Huang, Shenghao Fu, Yian Jin +10
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their d…
Fair Context Learning for Evidence-Balanced Test-Time Adaptation in Vision-Language Models
Sanggeon Yun, Ryozo Masukawa, SungHeon Jeong +3
Vision-Language Models (VLMs) such as CLIP enable strong zero-shot recognition but suffer substantial degradation under distribution shifts. Test-Time Adaptation (TTA) aims to impr…
HopFormer: Sparse Graph Transformers with Explicit Receptive Field Control
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa +2
Graph Transformers typically rely on explicit positional or structural encodings and dense global attention to incorporate graph topology. In this work, we show that neither is ess…
Internal Flow Signatures for Self-Checking and Refinement in LLMs
Sungheon Jeong, Sanggeon Yun, Ryozo Masukawa +3
Large language models can generate fluent answers that are unfaithful to the provided context, while many safeguards rely on external verification or a separate judge after generat…
Draft and Refine with Visual Experts
Sungheon Jeong, Ryozo Masukawa, Jihong Park +5
While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavi…