Nattanat Chatthee — accepted AI-conference papers

2023

Zero-guidance Segmentation Using Zero Segment Labels

Pitchaporn Rewatbowornwong, Nattanat Chatthee, Ekapol Chuangsuwanich, Supasorn Suwajanakorn

ICCV 2023poster

The joint visual-language model CLIP has enabled new and exciting applications, such as open-vocabulary segmentation, which can locate any segment given an arbitrary text query. In our research, we ask whether it is possible to discover semantic segments without any user guidance in the form of text…

Cited by 12 PDF ↗code ↗Scholar ↗

2022

Diffusion Autoencoders: Toward a Meaningful and Decodable Representation

Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, Supasorn Suwajanakorn

CVPR 2022oral

Diffusion probabilistic models (DPMs) have achieved remarkable quality in image generation that rivals GANs'. But unlike GANs, DPMs use a set of latent variables that lack semantic meaning and cannot serve as a useful representation for other tasks. This paper explores the possibility of using DPMs…

Cited by 456 PDF ↗code ↗Scholar ↗