1 code implementation • 9 Dec 2024 • Patrick Ramos, Nicolas Gonthier, Selina Khan, Yuta Nakashima, Noa Garcia
Object detection in art is a valuable tool for the digital humanities, as it allows for faster identification of objects in artistic and historical images compared to humans.
no code implementations • 22 Jul 2024 • Xiao Liu, Liangzhi Li, Tong Xiang, Fuying Ye, Lu Wei, Wangyue Li, Noa Garcia
Unlike conventional methods that target explicit malicious responses, our approach delves deeper into the nature of the information provided in responses.
no code implementations • CVPR 2024 • Tianwei Chen, Yusuke Hirota, Mayu Otani, Noa Garcia, Yuta Nakashima
We investigate the impact of deep generative models on potential social biases in upcoming computer vision models.
1 code implementation • 26 Mar 2024 • Wangyue Li, Liangzhi Li, Tong Xiang, Xiao Liu, Wei Deng, Noa Garcia
Additionally, we propose two methods to quantify the consistency and confidence of LLMs' output, which can be generalized to other QA evaluation benchmarks.
no code implementations • 5 Dec 2023 • Yankun Wu, Yuta Nakashima, Noa Garcia
Several studies have raised awareness about social biases in image generative models, demonstrating their predisposition towards stereotypes and imbalances.
1 code implementation • NeurIPS 2023 • Tong Xiang, Liangzhi Li, Wangyue Li, Mingbai Bai, Lu Wei, Bowen Wang, Noa Garcia
In an effort to minimize the reliance on human resources for performance evaluation, we offer off-the-shelf judgment models for automatically assessing the LF output of LLMs given benchmark questions.
1 code implementation • 20 Apr 2023 • Yankun Wu, Yuta Nakashima, Noa Garcia
The duality of content and style is inherent to the nature of art.
1 code implementation • CVPR 2023 • Yusuke Hirota, Yuta Nakashima, Noa Garcia
From this observation, we hypothesize that there are two types of gender bias affecting image captioning models: 1) bias that exploits context to predict gender, and 2) bias in the probability of generating certain (often stereotypical) words because of gender.
1 code implementation • CVPR 2023 • Noa Garcia, Yusuke Hirota, Yankun Wu, Yuta Nakashima
The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations.
no code implementations • 23 Aug 2022 • Tianwei Chen, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima, Hajime Nagahara
Is more data always better to train vision-and-language models?
no code implementations • 17 May 2022 • Yusuke Hirota, Yuta Nakashima, Noa Garcia
Our findings suggest that there are dangers associated to using VQA datasets without considering and dealing with the potentially harmful stereotypes.
1 code implementation • CVPR 2022 • Yusuke Hirota, Yuta Nakashima, Noa Garcia
We study societal bias amplification in image captioning.
no code implementations • 3 Feb 2022 • Nikolaos-Antonios Ypsilantis, Noa Garcia, Guangxing Han, Sarah Ibrahimi, Nanne van Noord, Giorgos Tolias
Testing is primarily performed on photos taken by museum guests depicting exhibits, which introduces a distribution shift between training and testing.
no code implementations • 26 Oct 2021 • Tianran Wu, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima, Haruo Takemura
Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip.
1 code implementation • ICCV 2021 • Zechen Bai, Yuta Nakashima, Noa Garcia
Have you ever looked at a painting and wondered what is the story behind it?
no code implementations • ACL 2021 • Jules Samaran, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima
The impressive performances of pre-trained visually grounded language models have motivated a growing body of research investigating what has been learned during the pre-training.
no code implementations • 25 Jun 2021 • Yusuke Hirota, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima, Ittetsu Taniguchi, Takao Onoye
This paper delves into the effectiveness of textual representations for image understanding in the specific context of VQA.
no code implementations • 25 May 2021 • Cheikh Brahim El Vaigh, Noa Garcia, Benjamin Renoust, Chenhui Chu, Yuta Nakashima, Hajime Nagahara
In this paper, we propose a novel use of a knowledge graph, that is constructed on annotated data and pseudo-labeled data.
no code implementations • 14 Jan 2021 • Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, Chenhui Chu
Visual Question Answering (VQA) is of tremendous interest to the research community with important applications such as aiding visually impaired users and image-based search.
no code implementations • 30 Sep 2020 • Nikolai Huckle, Noa Garcia, Yuta Nakashima
Art produced today, on the other hand, is numerous and easily accessible, through the internet and social networks that are used by professional and amateur artists alike to display their work.
1 code implementation • 28 Aug 2020 • Noa Garcia, Chentao Ye, Zihua Liu, Qingtao Hu, Mayu Otani, Chenhui Chu, Yuta Nakashima, Teruko Mitamura
Our dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions.
1 code implementation • ECCV 2020 • Noa Garcia, Yuta Nakashima
To understand movies, humans constantly reason over the dialogues and actions shown in specific scenes and relate them to the overall storyline already seen.
no code implementations • 17 Apr 2020 • Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima
We propose a novel video understanding task by fusing knowledge-based and video question answering.
no code implementations • 23 Oct 2019 • Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima
We propose a novel video understanding task by fusing knowledge-based and video question answering.
no code implementations • 17 Sep 2019 • Benjamin Renoust, Matheus Oliveira Franca, Jacob Chan, Noa Garcia, Van Le, Ayaka Uesaka, Yuta Nakashima, Hajime Nagahara, Jueren Wang, Yutaka Fujioka
While Buddhism has spread along the Silk Roads, many pieces of art have been displaced.
no code implementations • 24 Apr 2019 • Noa Garcia, Benjamin Renoust, Yuta Nakashima
In computer vision, visual arts are often studied from a purely aesthetics perspective, mostly by analysing the visual appearance of an artistic reproduction to infer its style, its author, or its representative features.
1 code implementation • 10 Apr 2019 • Noa Garcia, Benjamin Renoust, Yuta Nakashima
Whereas visual representations are able to capture information about the content and the style of an artwork, our proposed context-aware embeddings additionally encode relationships between different artistic attributes, such as author, school, or historical period.
no code implementations • 23 Oct 2018 • Noa Garcia, George Vogiatzis
Automatic art analysis has been mostly focused on classifying artworks into different artistic styles.
no code implementations • 19 Oct 2017 • Noa Garcia, George Vogiatzis
This work proposes a system for retrieving clothing and fashion products from video content.
no code implementations • ICLR 2018 • Noa Garcia, George Vogiatzis
Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the system.