8 citations · 15 across the 6 of their papers we have counts for
9 papers
Recursive Value Learning for Long-Horizon Offline Goal-Conditioned RL
Hyeonseong Jeon, Youngwoon Lee
Scaling offline goal-conditioned reinforcement learning (GCRL) to long-horizon tasks is difficult because (1) long-range value learning depends on shorter-range estimates that may…
Why Far Looks Up: Probing Spatial Representation in Vision-Language Models
Cheolhong Min, Jaeyun Jung, Daeun Lee +5
Vision-language models (VLMs) achieve strong performance on spatial reasoning benchmarks, yet it remains unclear whether this reflects structured 3D understanding or reliance on st…
IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
Sunghyun Baek, Jaemyung Yu, Seunghee Koh +3
Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich repr…
UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting
Geonuk Kim, Minhoi Kim, Kangil Lee +5
Although industrial inspection systems should be capable of recognizing unprecedented defects, most existing approaches operate under a closed-set assumption, which prevents them f…
Tree-Guided Diffusion Planner
Hyeonseong Jeon, Cheolhong Min, Jaesik Park
Planning with pretrained diffusion models has emerged as a promising approach for solving test-time guided control problems. Standard gradient guidance typically performs optimally…
Convergent Functions, Divergent Forms
Hyeonseong Jeon, Ainaz Eftekhar, Aaron Walsman +3
We introduce LOKI, a compute-efficient framework for co-designing morphologies and control policies that generalize across unseen tasks. Inspired by biological adaptation -- where…
Learning to Continually Learn with the Bayesian Principle
Soochan Lee, Hyeonseong Jeon, Jaehyeon Son +1
In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non…
T-GD: Transferable GAN-generated Images Detection Framework
Hyeonseong Jeon, Youngoh Bang, Junyaup Kim +1
Recent advancements in Generative Adversarial Networks (GANs) enable the generation of highly realistic images, raising concerns about their misuse for malicious purposes. Detectin…
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
Hyeonseong Jeon, Youngoh Bang, Simon S. Woo
Creating fake images and videos such as "Deepfake" has become much easier these days due to the advancement in Generative Adversarial Networks (GANs). Moreover, recent research suc…