5 citations · 13 across the 5 of their papers we have counts for
5 papers
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…
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…
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee +34
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less report…