528 citations · 541 across the 12 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
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…
Xi-Vector Embedding for Speaker Recognition
Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On th…
Using Multi-Resolution Feature Maps with Convolutional Neural Networks for Anti-Spoofing in ASV
Qiongqiong Wang, Kong Aik Lee, Takafumi Koshinaka
This paper presents a simple but effective method that uses multi-resolution feature maps with convolutional neural networks (CNNs) for anti-spoofing in automatic speaker verificat…
A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition
Qiongqiong Wang, Koji Okabe, Kong Aik Lee +1
This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing…
Unleashing the Unused Potential of I-Vectors Enabled by GPU Acceleration
Ville Vestman, Kong Aik Lee, Tomi H. Kinnunen +1
Speaker embeddings are continuous-value vector representations that allow easy comparison between voices of speakers with simple geometric operations. Among others, i-vector and x-…
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen +43
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which…
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA
Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
State-of-the-art speaker recognition systems comprise an x-vector (or i-vector) speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) backend.…
Attention Mechanism in Speaker Recognition: What Does It Learn in Deep Speaker Embedding?
Qiongqiong Wang, Koji Okabe, Kong Aik Lee +2
This paper presents an experimental study on deep speaker embedding with an attention mechanism that has been found to be a powerful representation learning technique in speaker re…
Attentive Statistics Pooling for Deep Speaker Embedding
Koji Okabe, Takafumi Koshinaka, Koichi Shinoda
This paper proposes attentive statistics pooling for deep speaker embedding in text-independent speaker verification. In conventional speaker embedding, frame-level features are av…