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Jinglong Yang

dblp:308/7307 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
language-based planning
0.812024
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
0.812024
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
0.812024
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
0.212024
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
YearPublicationVenuePosition
2024 Using Left and Right Brains Together: Towards Vision and Language Planning
abstract
Large 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
ICML6
2021 Novel GAN Inversion Model with Latent Space Constraints for Face Reconstruction
Jinglong Yang, Xiongwen Quan, Han Zhang 0017
ICONIP (3)1