4 citations · 9 across the 9 of their papers we have counts for
8 papers · 1 filter
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
Minhyun Lee, Seungho Lee, Song Park +3
Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions.…
SeiT++: Masked Token Modeling Improves Storage-efficient Training
Minhyun Lee, Song Park, Byeongho Heo +2
Recent advancements in Deep Neural Network (DNN) models have significantly improved performance across computer vision tasks. However, achieving highly generalizable and high-perfo…
SeiT: Storage-Efficient Vision Training with Tokens Using 1% of Pixel Storage
Song Park, Sanghyuk Chun, Byeongho Heo +2
We need billion-scale images to achieve more generalizable and ground-breaking vision models, as well as massive dataset storage to ship the images (e.g., the LAION-4B dataset need…
ECCV Caption: Correcting False Negatives by Collecting Machine-and-Human-verified Image-Caption Associations for MS-COCO
Sanghyuk Chun, Wonjae Kim, Song Park +2
Image-Text matching (ITM) is a common task for evaluating the quality of Vision and Language (VL) models. However, existing ITM benchmarks have a significant limitation. They have…
Few-shot Font Generation with Weakly Supervised Localized Representations
Song Park, Sanghyuk Chun, Junbum Cha +2
Automatic few-shot font generation aims to solve a well-defined, real-world problem because manual font designs are expensive and sensitive to the expertise of designers. Existing…
StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures
Sanghyuk Chun, Song Park
Recent powerful vision classifiers are biased towards textures, while shape information is overlooked by the models. A simple attempt by augmenting training images using the artist…
Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts
Song Park, Sanghyuk Chun, Junbum Cha +2
A few-shot font generation (FFG) method has to satisfy two objectives: the generated images should preserve the underlying global structure of the target character and present the…
Few-shot Font Generation with Localized Style Representations and Factorization
Song Park, Sanghyuk Chun, Junbum Cha +2
Automatic few-shot font generation is a practical and widely studied problem because manual designs are expensive and sensitive to the expertise of designers. Existing few-shot fon…