Built with and by Teycir Ben Soltane•
How to Use•FAQ•GitHub•arXiv.org•
Share:
ArXivCSExplorer
☆☆Bookmarks🏆RSSHow to UseFAQ
Home/Authors/Katsuki Chousa

Katsuki Chousa

2 indexed papers

Recent (6 mo)
2
With code
0
Influential cites
0
Benchmarked
0

Publications per year

2
26

Top categories

NLP×2Audio and Speech Processing×1AI×1Crypto×1Info Retrieval×1

Frequent co-authors

Hiroyuki Deguchi2×
Takatomo Kano1×
Marc Delcroix1×
Yusuke Sakai1×

Research Timeline

2026
One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness

The paper proposes a method to identify 'hub texts' that exploit vulnerabilities in cross-modal encoders, demonstrating that a single text can achieve unrealistically high similarity scores across diverse images in tasks like image captioning and retrieval.

Non-Autoregressive Minimum Bayes' Risk Decoding for Fast Speech Recognition

This paper proposes a novel non-autoregressive (NAR) decoding framework based on minimum Bayes' risk (MBR) for speech recognition, which outperforms previous NAR decoding and runs faster than autoregressive decoding.

Highlighted terms show continued research focus across papers

Papers

eess.AScs.CLEmpiricalRecentJun 16, 2026

Non-Autoregressive Minimum Bayes' Risk Decoding for Fast Speech Recognition

Hiroyuki Deguchi, Takatomo Kano, Katsuki Chousa, Marc Delcroix

This paper proposes a novel non-autoregressive (NAR) decoding framework based on minimum Bayes' risk (MBR) for speech recognition, which outperforms previous NAR decoding and runs faster than autoregr…

View →
cs.CLcs.AIcs.CRRecent
Apr 30, 2026

One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness

Hiroyuki Deguchi, Katsuki Chousa, Yusuke Sakai

The paper proposes a method to identify 'hub texts' that exploit vulnerabilities in cross-modal encoders, demonstrating that a single text can achieve unrealistically high similarity scores across div…

View →