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New framework uses text and images to assess image complexity

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Describe-to-Score (D2S) that integrates textual semantics with visual features to assess image complexity. This multimodal approach aims to capture high-level semantic cues that traditional visual-only methods miss. D2S uses caption-derived semantics during training to regularize visual complexity modeling, allowing for vision-only inference without additional overhead. The framework has demonstrated state-of-the-art performance on the IC9600 benchmark and shows competitiveness in no-reference image quality assessment tasks. AI

IMPACT This framework could improve image analysis tasks by incorporating semantic understanding into complexity assessment.

RANK_REASON The cluster contains an academic paper detailing a new framework for image complexity assessment. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework uses text and images to assess image complexity

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The cluster contains an academic paper detailing a new framework for image complexity assessment. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shipeng Liu, Liang Zhao, Dengfeng Chen, Zhonglin Zhang ·

    Describe-to-Score: A text-guided framework for image complexity assessment

    arXiv:2509.16609v2 Announce Type: replace Abstract: Accurately assessing image complexity (IC) is essential for many vision tasks, yet existing approaches rely almost exclusively on visual features and therefore fail to capture the high-level semantics that humans often use when …

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