EDBT 2026 Demo / reviewers in the wild / expert
Jinglong Yang
dblp:308/7307
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2024
0000-0002-5616-1861ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Planning, search and constraint satisfaction · 91% Vision and language · 9% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
language-based planning |
0.8 | 1 | 2024 | Using Left and Right Brains Together: Towards Vision and Language Planning · ICML 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning › language-based planning
vision-language planning |
0.8 | 1 | 2024 | Using Left and Right Brains Together: Towards Vision and Language Planning · ICML 2024 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
visual planning |
0.8 | 1 | 2024 | Using Left and Right Brains Together: Towards Vision and Language Planning · ICML 2024 |
Computer vision › Vision and language › vision-language model › multimodal large language model
multimodal large language model reasoning |
0.2 | 1 | 2024 | Using Left and Right Brains Together: Towards Vision and Language Planning · ICML 2024 |
Methods — techniques the papers use, named apart from their topics
large multimodal model · 0.8large language model · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Using Left and Right Brains Together: Towards Vision and Language PlanningabstractLarge Language Models (LLMs) and Large Multi-modality Models (LMMs) have demonstrated remarkable decision masking capabilities on a variety of tasks. However, they inherently operate planning within the language space, lacking the vision and spatial imagination ability. In contrast, humans utilize both left and right hemispheres of the brain for language and visual planning during the thinking process. Therefore, we introduce a novel vision-language planning framework in this work to perform concurrent visual and language planning for tasks with inputs of any form. Our framework incorporates visual planning to capture intricate environmental details, while language planning enhances the logical coherence of the overall system. We evaluate the effectiveness of our framework across vision-language tasks, vision-only tasks, and language-only tasks. The results demonstrate the superior performance of our approach, indicating that the integration of visual and language planning yields better contextually aware task execution. Jun Cen, Chenfei Wu, Xiao Liu 0029, Shengming Yin, Yixuan Pei, Jinglong Yang, Qifeng Chen 0001, Nan Duan 0001 |
ICML | 6 |
| 2021 | Novel GAN Inversion Model with Latent Space Constraints for Face Reconstruction
Jinglong Yang, Xiongwen Quan, Han Zhang 0017 |
ICONIP (3) | 1 |