5 citations · 6 across the 8 of their papers we have counts for
9 papers
SDS-LoRA: Overcoming Anisotropic Gradient Scaling in Low-Rank Adaptation
Junghun Oh, Sungyong Baik, Kyoung Mu Lee
Low-Rank Adaptation (LoRA) enables efficient adaptation of large pretrained models to downstream tasks by parameterizing weight updates with low-rank matrices. In this paper, we in…
MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
Peng Xu, Shengwu Xiong, Jiajun Zhang +125
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We…
Exploiting Diffusion Prior for Task-driven Image Restoration
Jaeha Kim, Junghun Oh, Kyoung Mu Lee
Task-driven image restoration (TDIR) has recently emerged to address performance drops in high-level vision tasks caused by low-quality (LQ) inputs. Previous TDIR methods struggle…
Find A Winning Sign: Sign Is All We Need to Win the Lottery
Junghun Oh, Sungyong Baik, Kyoung Mu Lee
The Lottery Ticket Hypothesis (LTH) posits the existence of a sparse subnetwork (a.k.a. winning ticket) that can generalize comparably to its over-parameterized counterpart when tr…
CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning
Junghun Oh, Sungyong Baik, Kyoung Mu Lee
Aiming to incrementally learn new classes with only few samples while preserving the knowledge of base (old) classes, few-shot class-incremental learning (FSCIL) faces several chal…
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
Jaeha Kim, Junghun Oh, Kyoung Mu Lee
In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within…
Attentive Fine-Grained Structured Sparsity for Image Restoration
Junghun Oh, Heewon Kim, Seungjun Nah +3
Image restoration tasks have witnessed great performance improvement in recent years by developing large deep models. Despite the outstanding performance, the heavy computation dem…
Batch Normalization Tells You Which Filter is Important
Junghun Oh, Heewon Kim, Sungyong Baik +2
The goal of filter pruning is to search for unimportant filters to remove in order to make convolutional neural networks (CNNs) efficient without sacrificing the performance in the…
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
Cheeun Hong, Heewon Kim, Sungyong Baik +2
Quantizing deep convolutional neural networks for image super-resolution substantially reduces their computational costs. However, existing works either suffer from a severe perfor…