no code implementations • Findings (EMNLP) 2021 • Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article.
no code implementations • 14 May 2024 • Boqi Chen, Kristóf Marussy, Oszkár Semeráth, Gunter Mussbacher, Dániel Varró
This poses a significant problem for GCNs intended to be used in critical applications, which need to provide certifiably robust services even in the presence of adversarial perturbations.
1 code implementation • 18 Mar 2024 • Boqi Chen, Junier Oliva, Marc Niethammer
Medical records often consist of different modalities, such as images, text, and tabular information.
1 code implementation • 4 Sep 2023 • Boqi Chen, Fandi Yi, Dániel Varró
Our result reveals the following: (1) Even without explicit training on the dataset, the prompting approach outperforms fine-tuning-based approaches.
no code implementations • 17 Mar 2023 • Boqi Chen, Marc Niethammer
We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities.
1 code implementation • 17 Jan 2023 • Boqi Chen, Kua Chen, Yujing Yang, Afshin Amini, Bharat Saxena, Cecilia Chávez-García, Majid Babaei, Amir Feizpour, Dániel Varró
In this paper, we propose a general architecture to incorporate knowledge graphs for XIR in various steps of the retrieval process.
no code implementations • 3 Jan 2023 • Boqi Chen, Kevin Thandiackal, Pushpak Pati, Orcun Goksel
In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains.
no code implementations • 26 Apr 2022 • Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani, Orcun Goksel
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.