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7 papers
Reference-Free Image Quality Assessment for Virtual Try-On via Human Feedback
Yuki Hirakawa, Takashi Wada, Ryotaro Shimizu +6
As virtual try-on (VTON) systems become increasingly important in fashion e-commerce, there is a growing need for reliable reference-free evaluation methods, since ground-truth ima…
Transferring Visual Explainability of Self-Explaining Models to Prediction-Only Models without Additional Training
Yuya Yoshikawa, Ryotaro Shimizu, Takahiro Kawashima +1
In image classification scenarios where both prediction and explanation efficiency are required, self-explaining models that perform both tasks in a single inference are effective.…
Static Word Embeddings for Sentence Semantic Representation
Takashi Wada, Yuki Hirakawa, Ryotaro Shimizu +2
We propose new static word embeddings optimised for sentence semantic representation. We first extract word embeddings from a pre-trained Sentence Transformer, and improve them wit…
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Ryotaro Shimizu, Takashi Wada, Yu Wang +9
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between t…
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property
Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu +1
Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features…
An Empirical Analysis of GPT-4V's Performance on Fashion Aesthetic Evaluation
Yuki Hirakawa, Takashi Wada, Kazuya Morishita +4
Fashion aesthetic evaluation is the task of estimating how well the outfits worn by individuals in images suit them. In this work, we examine the zero-shot performance of GPT-4V on…