Free-Grained Hierarchical Recognition

• URL: http://arxiv.org/abs/2510.14737v1
• Date: Thu, 16 Oct 2025 14:35:18 GMT
• Title: Free-Grained Hierarchical Recognition
• Authors: Seulki Park, Zilin Wang, Stella X. Yu,
• Abstract summary: Real-world supervision varies in granularity, influenced by image quality, annotator expertise, and task demands.<n>We introduce ImageNet-F, a large-scale benchmark structured into cognitively inspired basic, subordinate, and fine-grained levels.<n>Using CLIP as a proxy for semantic ambiguity, we simulate realistic, mixed-granularity labels.<n>We develop methods that enhance semantic guidance via pseudo-attributes from vision-language models and visual guidance via semi-supervised learning.
• Score: 32.11403864881941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hierarchical image classification predicts labels across a semantic taxonomy, but existing methods typically assume complete, fine-grained annotations, an assumption rarely met in practice. Real-world supervision varies in granularity, influenced by image quality, annotator expertise, and task demands; a distant bird may be labeled Bird, while a close-up reveals Bald eagle. We introduce ImageNet-F, a large-scale benchmark curated from ImageNet and structured into cognitively inspired basic, subordinate, and fine-grained levels. Using CLIP as a proxy for semantic ambiguity, we simulate realistic, mixed-granularity labels reflecting human annotation behavior. We propose free-grain learning, with heterogeneous supervision across instances. We develop methods that enhance semantic guidance via pseudo-attributes from vision-language models and visual guidance via semi-supervised learning. These, along with strong baselines, substantially improve performance under mixed supervision. Together, our benchmark and methods advance hierarchical classification under real-world constraints.

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