Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2025
Abstract
Scene context prediction, which seeks to infer unknown contextual information from isolated object properties, currently faces limitations due to predominant reliance on pixel-wise supervision that overlooks real-world context priors. To address this, we present ContX, a context-prior-driven, coarse-to-fine model. ContX distinctively integrates explicit linguistic-contextual knowledge in two key ways. First, it proposes a linguistic guided context bank, leveraging linguistic-statistical contextual data to guide the rationality of segmentation shapes and foster meaningful inter-class contextual interactions. Second, ContX augments contextual comprehension by correlating layouts with linguistic descriptions, enhancing layout perception through a multi-modal strategy. Comprehensive experiments demonstrate ContX's superiority and versatility, outperforming current state-of-the-art methods in both qualitative and quantitative assessments. The code is available at https://github.com/liangjingxin4747/ContX.
Keywords
Generative adversarial network, Layout prediction, Prior knowledge, Scene context, Scene understanding
Discipline
Artificial Intelligence and Robotics | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Pattern Recognition
Volume
168
First Page
1
Last Page
13
ISSN
0031-3203
Identifier
10.1016/j.patcog.2025.111852
Publisher
Elsevier
Citation
LIANG, Jingxin; XU, Yangyang; SONG, Haorui; LU, Yuan; DENG, Yuhui; LONG, Yiyi; HUANG, Yan; LIU, Shengxin; JIAO, Jianbo; and Shengfeng HE.
ContX: Scene context prediction via context bank and layout perception. (2025). Pattern Recognition. 168, 1-13.
Available at: https://ink.library.smu.edu.sg/sis_research/10233
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.patcog.2025.111852