arXiv Machine Learning By Takuro Kawada, Shunsuke Kitada, Hitoshi Iyatomi

GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers

Read the original on arXiv Machine Learning →

arXiv:2608. 05478v1 Announce Type: cross Abstract: Graphical Abstracts (GAs) visually summarize the key findings of academic papers, playing a crucial role in facilitating the understanding of research content.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI
Jun 12

VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents

arXiv:2602. 00122v3 Announce Type: replace-cross Abstract: In recent years, image editing models have made significant progress, enabling users to manipulate visual content in a flexible and interactive manner through natural language instructions.

By Hongzhu Yi, Yujia Yang, Yuanxiang Wang, Tong Li, Zhenyu Guan, Tianyu Zong, Jiahuan Chen, Chenxi Bao, Tiankun Yang, Haopeng Jin, Yixuan Yuan, Xinming Wang, Tao Yu, Ruilin Gao, Ruiwen Tao, Haijin Liang, Jin Ma, Jinwen Luo, Yeshani, Xinyu Zuo, Jungang Xu
arXiv AI
Jun 2

TECCI: Tricky Edits of Collected and Curated Images

arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.

By Aishwarya Agrawal, Roy Hirsch, Yasumasa Onoe, Sherry Ben, Jason Baldridge