58 citations · 175 across the 28 of their papers we have counts for
35 papers · 1 filter
Isotropic Embedding Perturbations for Robust Vision Language Encoders
Hyesong Choi, Daeun Kim, Song Park +5
Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer stro…
Which Concepts to Forget and How to Refuse? Decomposing Concepts for Continual Unlearning in Large Vision-Language Models
Hyundong Jin, Dongyoon Han, Eunwoo Kim
Continual unlearning poses the challenge of enabling large vision-language models to selectively refuse specific image-instruction pairs in response to sequential deletion requests…
Grounding World Simulation Models in a Real-World Metropolis
Junyoung Seo, Hyunwook Choi, Minkyung Kwon +10
What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificia…
On the Reliability of Cue Conflict and Beyond
Pum Jun Kim, Seung-Ah Lee, Seongho Park +2
Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes. The cue-conflict benchmark has been influential in pro…
RL makes MLLMs see better than SFT
Junha Song, Sangdoo Yun, Dongyoon Han +2
A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarka…
Exploring Conditions for Diffusion models in Robotic Control
Heeseong Shin, Byeongho Heo, Dongyoon Han +2
While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we…