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Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
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Related Experiment Video

Updated: Sep 19, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Learning contrastive semantic decomposition for visual grounding.

Jie Wu1, Chunlei Wu1, Yiwei Wei2

  • 1Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new Contrastive Semantic Decomposition network for Visual Grounding (CSDVG). CSDVG improves accuracy by better separating and combining visual and language features for object identification.

Keywords:
Multimodal information fusionSemantics decompositionVisual grounding

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Area of Science:

  • Computer Vision
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Visual grounding links natural language descriptions to image regions.
  • Current methods use separate encoders, potentially missing shared attributes and causing redundant fusion.

Purpose of the Study:

  • To propose a novel Contrastive Semantic Decomposition network for Visual Grounding (CSDVG).
  • To effectively decompose shared-specific semantic features and model cross-modality features for improved visual grounding.

Main Methods:

  • Developed CSDVG with an associated semantic branch for shared features and an independent semantic branch for specific features.
  • Introduced a relevance-driven loss function to balance shared and specific feature learning.

Main Results:

  • CSDVG demonstrated superior performance compared to existing approaches.
  • Experiments showed effectiveness across all tested datasets.

Conclusions:

  • The proposed CSDVG effectively decomposes and models cross-modality features.
  • CSDVG addresses limitations of independent encoders and redundant fusion in visual grounding tasks.