activity
20182026
most citedI4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

3 citations · 6 across the 10 of their papers we have counts for

collaborators

14 papers

cs.CV2026

SIFT-VTON: Geometric Correspondence Supervision on Cross-Attention for Virtual Try-On

Kosuke Takemoto, Takafumi Koshinaka

Diffusion-based virtual try-on methods achieve photorealistic synthesis through cross-attention mechanisms that transfer garment features to target body regions. However, these app…

cs.CV2025

HYB-VITON: A Hybrid Approach to Virtual Try-On Combining Explicit and Implicit Warping

Kosuke Takemoto, Takafumi Koshinaka

Virtual try-on systems have significant potential in e-commerce, allowing customers to visualize garments on themselves. Existing image-based methods fall into two categories: thos…

cs.CV2024★ 1 cited

Reading Is Believing: Revisiting Language Bottleneck Models for Image Classification

Honori Udo, Takafumi Koshinaka

We revisit language bottleneck models as an approach to ensuring the explainability of deep learning models for image classification. Because of inevitable information loss incurre…

eess.AS2023

Generalized domain adaptation framework for parametric back-end in speaker recognition

Qiongqiong Wang, Koji Okabe, Kong Aik Lee +1

State-of-the-art speaker recognition systems comprise a speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness of t…

cs.CV2023★ 1 cited

Image Captioners Sometimes Tell More Than Images They See

Honori Udo, Takafumi Koshinaka

Image captioning, a.k.a. "image-to-text," which generates descriptive text from given images, has been rapidly developing throughout the era of deep learning. To what extent is the…

cs.SD2021

Task-aware Warping Factors in Mask-based Speech Enhancement

Qiongqiong Wang, Kong Aik Lee, Takafumi Koshinaka +2

This paper proposes the use of two task-aware warping factors in mask-based speech enhancement (SE). One controls the balance between speech-maintenance and noise-removal in traini…