Search Results for author: Yunsung Lee

Found 12 papers, 5 papers with code

Multi-Architecture Multi-Expert Diffusion Models

no code implementations8 Jun 2023 Yunsung Lee, Jin-Young Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, Seungtaek Choi

In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME).

Denoising Image Generation

ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function

no code implementations7 Jun 2023 Jin-Young Kim, Soonwoo Kwon, Hyojun Go, Yunsung Lee, Seungtaek Choi

Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones.

Contrastive Learning Representation Learning

Addressing Negative Transfer in Diffusion Models

1 code implementation NeurIPS 2023 Hyojun Go, Jinyoung Kim, Yunsung Lee, SeungHyun Lee, Shinhyeok Oh, Hyeongdon Moon, Seungtaek Choi

Through this, our approach addresses the issue of negative transfer in diffusion models by allowing for efficient computation of MTL methods.

Clustering Denoising +1

Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling

no code implementations4 Oct 2022 Yunsung Lee, Gyuseong Lee, Kwangrok Ryoo, Hyojun Go, JiHye Park, Seungryong Kim

In addition, through Fourier analysis of feature maps, the model's response patterns according to signal frequency changes, we observe which inductive bias is advantageous for each data scale.

Inductive Bias Scheduling

CATs: Cost Aggregation Transformers for Visual Correspondence

1 code implementation NeurIPS 2021 Seokju Cho, Sunghwan Hong, Sangryul Jeon, Yunsung Lee, Kwanghoon Sohn, Seungryong Kim

We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.

Semantic correspondence

Robust Long-Term Object Tracking via Improved Discriminative Model Prediction

1 code implementation11 Aug 2020 Seokeon Choi, Junhyun Lee, Yunsung Lee, Alexander Hauptmann

We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker.

Object Tracking

Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence

no code implementations CVPR 2020 Junsoo Lee, Eungyeup Kim, Yunsung Lee, Dongjun Kim, Jaehyuk Chang, Jaegul Choo

However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e. g., coloring a sketch of an originally blue car given a reference green car).

Colorization Image Colorization +1

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