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Is Internal State Feedback in an E-learning Environment Acceptable to People?
In on-demand e-learning environments, the lack of direct intervention can lead to a decline in learners’ engagement. To address …
Atsushi Ashida
,
Ryosuke Kawamura
,
Shizuka Shirai
,
Noriko Takemura
,
Mehrasa Alizadeh
,
Hideaki Hayashi
,
Hajime Nagahara
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URL
MIDAS: Mixing Ambiguous Data With Soft Labels for Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, …
Ryosuke Kawamura
,
Hideaki Hayashi
,
Noriko Takemura
,
Hajime Nagahara
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Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting
Facial expression spotting is a significant but challenging task in facial expression analysis. The accuracy of expression spotting is …
Yicheng Deng
,
Hideaki Hayashi
,
Hajime Nagahara
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URL
Pseudo-Label Learning with Calibrated Confidence Using an Energy-Based Model
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores …
Masahito Toba
,
Seiichi Uchida
,
Hideaki Hayashi
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Revisiting Pixel-Level Contrastive Pre-Training on Scene Images
Contrastive image representation learning through instance discrimination has shown impressive transfer performance. Recent strategies …
Zongshang Pang
,
Yuta Nakashima
,
Mayu Otani
,
Hajime Nagahara
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URL
ICDAR’23: Intelligent Cross-Data Analysis and Retrieval
Recently, there has been an increased interest in cross-data research problems, such as predicting air quality using life logging …
Guillaume Habault
,
Minh-Son Dao
,
Michael Alexander Riegler
,
Duc-Tien Dang-Nguyen
,
Yuta Nakashima
,
Cathal Gurrin
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Learning bottleneck concepts in image classification
Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address …
Bowen Wang
,
Liangzhi Li
,
Yuta Nakashima
,
Hajime Nagahara
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Model-agnostic gender debiased image captioning
Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate …
Yusuke Hirota
,
Yuta Nakashima
,
Noa Garcia
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Not only generative art: Stable diffusion for content-style disentanglement in art analysis
The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers …
Yankun Wu
,
Yuta Nakashima
,
Noa Garcia
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Toward verifiable and reproducible human evaluation for text-to-image generation
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process …
Mayu Otani
,
Riku Togashi
,
Yu Sawai
,
Ryosuke Ishigami
,
Yuta Nakashima
,
Esa Rahtu
,
Janne Heikkilä
,
Shin’ichi Satoh
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