1 code implementation • CVPR 2024 • Junbum Cha, Wooyoung Kang, Jonghwan Mun, Byungseok Roh
In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities.
Ranked #2 on
Science Question Answering
on ScienceQA
(using extra training data)
no code implementations • 4 Dec 2023 • Sunghun Kang, Junbum Cha, Jonghwan Mun, Byungseok Roh, Chang D. Yoo
Specifically, the proposed method aims to learn arbitrary image-to-text mapping for pseudo-labeling of arbitrary concepts, named Pseudo-Labeling for Arbitrary Concepts (PLAC).
1 code implementation • CVPR 2023 • Junbum Cha, Jonghwan Mun, Byungseok Roh
Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task.
Ranked #2 on
Semantic Segmentation
on CC3M-TagMask
Contrastive Learning
Open Vocabulary Semantic Segmentation
+4
1 code implementation • 21 Mar 2022 • Junbum Cha, Kyungjae Lee, Sungrae Park, Sanghyuk Chun
Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains.
Ranked #3 on
Domain Generalization
on TerraIncognita
2 code implementations • 22 Dec 2021 • Song Park, Sanghyuk Chun, Junbum Cha, Bado Lee, Hyunjung Shim
Existing methods learn to disentangle style and content elements by developing a universal style representation for each font style.
no code implementations • 21 Nov 2021 • Yunsung Lee, Teakgyu Hong, Han-Cheol Cho, Junbum Cha, Seungryong Kim
Compared to previous works, our method shows better or comparable performance on dense prediction fine-tuning tasks.
4 code implementations • ICCV 2021 • Song Park, Sanghyuk Chun, Junbum Cha, Bado Lee, Hyunjung Shim
MX-Font extracts multiple style features not explicitly conditioned on component labels, but automatically by multiple experts to represent different local concepts, e. g., left-side sub-glyph.
4 code implementations • NeurIPS 2021 • Junbum Cha, Sanghyuk Chun, Kyungjae Lee, Han-Cheol Cho, Seunghyun Park, Yunsung Lee, Sungrae Park
Domain generalization (DG) methods aim to achieve generalizability to an unseen target domain by using only training data from the source domains.
Ranked #25 on
Domain Generalization
on TerraIncognita
1 code implementation • COLING 2020 • Sungrae Park, Geewook Kim, Junyeop Lee, Junbum Cha, Ji-Hoon Kim, Hwalsuk Lee
This paper introduces a method that efficiently reduces the computational cost and parameter size of Transformer.
3 code implementations • 23 Sep 2020 • Song Park, Sanghyuk Chun, Junbum Cha, Bado Lee, Hyunjung Shim
However, learning component-wise styles solely from reference glyphs is infeasible in the few-shot font generation scenario, when a target script has a large number of components, e. g., over 200 for Chinese.
3 code implementations • ECCV 2020 • Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee, Seonghyeon Kim, Hwalsuk Lee
By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples.
no code implementations • 25 Sep 2019 • Sungrae Park, Geewook Kim, Junyeop Lee, Junbum Cha, Ji-Hoon Kim Hwalsuk Lee
When compared to Transformers with a comparable number of parameters and time complexity, the proposed model shows better performance.