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Open-Set Domain Adaptation with Visual-Language Foundation Models
Qing Yu, Go Irie, Kiyoharu Aizawa
Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge obtained from a source domain with labeled data to a target domain with unlabeled dat…
Manga109Dialog: A Large-scale Dialogue Dataset for Comics Speaker Detection
Yingxuan Li, Kiyoharu Aizawa, Yusuke Matsui
The expanding market for e-comics has spurred interest in the development of automated methods to analyze comics. For further understanding of comics, an automated approach is need…
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
Atsuyuki Miyai, Qing Yu, Go Irie +1
We present a novel vision-language prompt learning approach for few-shot out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from classes that are…
Guided Image Synthesis via Initial Image Editing in Diffusion Model
Jiafeng Mao, Xueting Wang, Kiyoharu Aizawa
Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images. While previous research has primarily focused on improving the control of…
GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection
Atsuyuki Miyai, Qing Yu, Go Irie +1
Zero-shot out-of-distribution (OOD) detection is a task that detects OOD images during inference with only in-distribution (ID) class names. Existing methods assume ID images conta…
Comprehensive Comparisons of Uniform Quantization in Deep Image Compression
Koki Tsubota, Kiyoharu Aizawa
In deep image compression, uniform quantization is applied to latent representations obtained by using an auto-encoder architecture for reducing bits and entropy coding. Quantizati…