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How to make effective use of domain experts for image classification? — arXiv:2609.17749

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.17749
License (as archived): CC0-1.0
Archived: 2026-10-15T16:16:10.805Z
SHA-256 of the archived text: c39775331c7712a7a78e0d98a25730a8807a7ef851ed2a4f1b3ad66c50820daa
Stable URL of this page: https://metraipolis.com/documento/dc_2ecd44c070bf

How to cite

How to make effective use of domain experts for image classification? — arXiv:2609.17749 — arXiv (en), https://arxiv.org/abs/2609.17749 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ecd44c070bf (Metraipolis archive dc_2ecd44c070bf, sha256 c39775331c7712a7a78e0d98a25730a8807a7ef851ed2a4f1b3ad66c50820daa)

BibTeX

@misc{dc2ecd44c070bf,
  title = {How to make effective use of domain experts for image classification? — arXiv:2609.17749},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.17749},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ecd44c070bf; sha256 c39775331c7712a7a78e0d98a25730a8807a7ef851ed2a4f1b3ad66c50820daa},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.17749. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A lot of expectations have been put for years on integrating domain expert knowledge in image classification models. Several approaches have been explored, Concept Bottleneck Models (CBMs) opened up a new avenue of research leading to many variants, and more recently to Concept-based Embedding Models (CEMs). CBM consider binary encoding of each concept, while CEM expands this idea by embedding each concept through two vectors. However in real-life scenarii, domain experts' knowledge is usually organized in concepts determined by various attributes, each attribute encoded either with numerical values, or range of values, or binary values, or categorical values. In this work, we first finetune an image feature extractor for classifying attributes representing the downstream object classes, where the class attributes have been specified by experts under various encoding formats. A classification head is then learnt from these various attributes to categorize target objects. We experimentally show that it improves the classification for three datasets: Kaggle fish dataset, AWA2 and a more challenging new wood charcoal dataset. We then propose an automatic selection of potential missclassified data. In this second step, experts are asked for those data to eventually modify the predicted attributes in order to improve the classification.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.17749 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T15:45:19.721Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).