5 citations · 15 across the 11 of their papers we have counts for
12 papers
Laguna M.1/XS.2 Technical Report
Julien Abadji, Marah Abdin, Connor Adams +93
We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has B total parameters (B activated per tok…
Understanding Differential Transformer Unchains Pretrained Self-Attentions
Chaerin Kong, Jiho Jang, Nojun Kwak
Differential Transformer has recently gained significant attention for its impressive empirical performance, often attributed to its ability to perform noise canceled attention. Ho…
Fashion Style Editing with Generative Human Prior
Chaerin Kong, Seungyong Lee, Soohyeok Im +1
Image editing has been a long-standing challenge in the research community with its far-reaching impact on numerous applications. Recently, text-driven methods started to deliver p…
ConcatPlexer: Additional Dim1 Batching for Faster ViTs
Donghoon Han, Seunghyeon Seo, Donghyeon Jeon +3
Transformers have demonstrated tremendous success not only in the natural language processing (NLP) domain but also the field of computer vision, igniting various creative approach…
AADiff: Audio-Aligned Video Synthesis with Text-to-Image Diffusion
Seungwoo Lee, Chaerin Kong, Donghyeon Jeon +1
Recent advances in diffusion models have showcased promising results in the text-to-video (T2V) synthesis task. However, as these T2V models solely employ text as the guidance, the…
Analyzing Multimodal Objectives Through the Lens of Generative Diffusion Guidance
Chaerin Kong, Nojun Kwak
Recent years have witnessed astonishing advances in the field of multimodal representation learning, with contrastive learning being the cornerstone for major breakthroughs. Latest…
Unifying Vision-Language Representation Space with Single-tower Transformer
Jiho Jang, Chaerin Kong, Donghyeon Jeon +2
Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an im…
Leveraging Off-the-shelf Diffusion Model for Multi-attribute Fashion Image Manipulation
Chaerin Kong, DongHyeon Jeon, Ohjoon Kwon +1
Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions. Previous works typically employ c…
Towards Efficient Neural Scene Graphs by Learning Consistency Fields
Yeji Song, Chaerin Kong, Seoyoung Lee +2
Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video)…
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis
Chaerin Kong, Nojun Kwak
The capacity to learn incrementally from an online stream of data is an envied trait of human learners, as deep neural networks typically suffer from catastrophic forgetting and st…
Self-Distilled Self-Supervised Representation Learning
Jiho Jang, Seonhoon Kim, Kiyoon Yoo +3
State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN…
Few-shot Image Generation with Mixup-based Distance Learning
Chaerin Kong, Jeesoo Kim, Donghoon Han +1
Producing diverse and realistic images with generative models such as GANs typically requires large scale training with vast amount of images. GANs trained with limited data can ea…