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Withdrawn paper details contrastive learning for image complexity

By PulseAugur Editorial · [1 sources] ·

A research paper, now withdrawn, introduced CLIC (Contrastive Learning for Image Complexity), a framework utilizing contrastive learning to represent image complexity. The study proposed Random Crop and Mix (RCM) to generate diverse training samples from multi-scale local image crops, aiming to avoid manual annotation costs and human subjective biases. Experiments showed CLIC's performance was comparable to state-of-the-art supervised methods and could improve computer vision task performance. AI

IMPACT This research, though withdrawn, explored novel methods for image complexity representation that could inform future computer vision model development.

RANK_REASON The item is a withdrawn academic paper detailing a novel method for image complexity representation. [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 →

Withdrawn paper details contrastive learning for image complexity

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The item is a withdrawn academic paper detailing a novel method for image complexity representation. [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, Zhanping Song ·

    Contrastive Learning for Image Complexity Representation

    arXiv:2408.03230v2 Announce Type: replace Abstract: Quantifying and evaluating image complexity can be instrumental in enhancing the performance of various computer vision tasks. Supervised learning can effectively learn image complexity features from well-annotated datasets. How…

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