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About this paper
Yun Xing, Jian Kang, Aoran Xiao, Jiahao Nie, Ling Shao, Shijian Lu
Researchers at Nanyang Technological University introduce Concept Curation (CoCu), a three-stage pipeline designed to bridge the semantic gap in language-supervised semantic segmentation by enriching image captions with missing visual concepts. CoCu achieved an average mIoU of 27.7% on eight benchmarks and showed faster pre-training convergence, yielding more robust and generalizable vision-language representations.
Key insights
- Addressing the semantic gap in image-text data is critical for developing more robust vision-language representations in language-supervised semantic segmentation.
- Leveraging vision-driven expansion to identify concepts from visually similar images effectively mitigates semantic bias and uncovers inconspicuous visual elements often missed by original captions.
- Combining a non-biased relevancy ranking with cluster-guided sampling ensures that expanded textual supervision is both accurate and semantically diverse, promoting more comprehensive and efficient model learning.
Summary by alphaXiv.
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