36 citations · 98 across the 19 of their papers we have counts for
19 papers
ACT2G: Attention-based Contrastive Learning for Text-to-Gesture Generation
Hitoshi Teshima, Naoki Wake, Diego Thomas +3
Recent increase of remote-work, online meeting and tele-operation task makes people find that gesture for avatars and communication robots is more important than we have thought. I…
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis
Yankun Wu, Yuta Nakashima, Noa Garcia
The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of…
Learning Bottleneck Concepts in Image Classification
Bowen Wang, Liangzhi Li, Yuta Nakashima +1
Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel…
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
Noa Garcia, Yusuke Hirota, Yankun Wu +1
The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations. It is known that even small but…
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Mayu Otani, Riku Togashi, Yu Sawai +5
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. How…
Model-Agnostic Gender Debiased Image Captioning
Yusuke Hirota, Yuta Nakashima, Noa Garcia
Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate such gender bias in image captioning models.…
Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization
Zongshang Pang, Yuta Nakashima, Mayu Otani +1
Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Unsupervised methods usually rely on heuristic training…
Deep Gesture Generation for Social Robots Using Type-Specific Libraries
Hitoshi Teshima, Naoki Wake, Diego Thomas +3
Body language such as conversational gesture is a powerful way to ease communication. Conversational gestures do not only make a speech more lively but also contain semantic meanin…
Learning More May Not Be Better: Knowledge Transferability in Vision and Language Tasks
Tianwei Chen, Noa Garcia, Mayu Otani +3
Is more data always better to train vision-and-language models? We study knowledge transferability in multi-modal tasks. The current tendency in machine learning is to assume that…
Gender and Racial Bias in Visual Question Answering Datasets
Yusuke Hirota, Yuta Nakashima, Noa Garcia
Vision-and-language tasks have increasingly drawn more attention as a means to evaluate human-like reasoning in machine learning models. A popular task in the field is visual quest…
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval
Riku Togashi, Mayu Otani, Yuta Nakashima +3
Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applic…
Quantifying Societal Bias Amplification in Image Captioning
Yusuke Hirota, Yuta Nakashima, Noa Garcia
We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and…
Optimal Correction Cost for Object Detection Evaluation
Mayu Otani, Riku Togashi, Yuta Nakashima +3
Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms o…
Transferring Domain-Agnostic Knowledge in Video Question Answering
Tianran Wu, Noa Garcia, Mayu Otani +3
Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formul…
Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation
Zechen Bai, Yuta Nakashima, Noa Garcia
Have you ever looked at a painting and wondered what is the story behind it? This work presents a framework to bring art closer to people by generating comprehensive descriptions o…
Built Year Prediction from Buddha Face with Heterogeneous Labels
Yiming Qian, Cheikh Brahim El Vaigh, Yuta Nakashima +3
Buddha statues are a part of human culture, especially of the Asia area, and they have been alongside human civilisation for more than 2,000 years. As history goes by, due to wars,…
PoseRN: A 2D pose refinement network for bias-free multi-view 3D human pose estimation
Akihiko Sayo, Diego Thomas, Hiroshi Kawasaki +2
We propose a new 2D pose refinement network that learns to predict the human bias in the estimated 2D pose. There are biases in 2D pose estimations that are due to differences betw…
A Picture May Be Worth a Hundred Words for Visual Question Answering
Yusuke Hirota, Noa Garcia, Mayu Otani +4
How far can we go with textual representations for understanding pictures? In image understanding, it is essential to use concise but detailed image representations. Deep visual fe…
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Liangzhi Li, Manisha Verma, Bowen Wang +3
The status of retinal arteriovenous crossing is of great significance for clinical evaluation of arteriolosclerosis and systemic hypertension. As an ophthalmology diagnostic criter…