10 papers
Mosaic: Compositional Multi-Concept Erasure via Vector Field Blending
Junseok Ko, Jungwoo Kim, Jong-Seok Lee
Concept erasure has emerged as a key research direction for ensuring safe and ethical image synthesis in Text-to-Image (T2I) models. While existing studies have explored concept er…
OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance
Yeo Jeong Park, Hyemi Jang, Minseo Choi +3
Omni-modal large language models have demonstrated remarkable potential in holistic multimodal understanding; however, the token explosion caused by high-resolution audio and video…
Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee
Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines and progressive coding schemes.…
Sparsely-Supervised Data Assimilation via Physics-Informed Schrödinger Bridge
Dohyun Bu, Chanho Kim, Seokun Choi +1
Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while…
Steering Away from Memorization: Reachability-Constrained Reinforcement Learning for Text-to-Image Diffusion
Sathwik Karnik, Juyeop Kim, Sanmi Koyejo +2
Text-to-image diffusion models often memorize training data, revealing a fundamental failure to generalize beyond the training set. Current mitigation strategies typically sacrific…
Progressive Learned Image Compression for Machine Perception
Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee
Recent advances in learned image codecs have extended from human perception toward machine perception However, progressive image compression with fine granular scalability (FGS)-wh…