Metraipolis › archive › dc_a06eb4abc575

Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images — arXiv:2608.12230

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.12230
License (as archived): CC0-1.0
Archived: 2026-10-19T05:16:33.687Z
SHA-256 of the archived text: 6aa51f8a279bfb3f576c378ae273dfa4c13e09d027cdc7c97cbe26f72e6b4591
Stable URL of this page: https://metraipolis.com/documento/dc_a06eb4abc575

How to cite

Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images — arXiv:2608.12230 — arXiv (en), https://arxiv.org/abs/2608.12230 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a06eb4abc575 (Metraipolis archive dc_a06eb4abc575, sha256 6aa51f8a279bfb3f576c378ae273dfa4c13e09d027cdc7c97cbe26f72e6b4591)

BibTeX

@misc{dca06eb4abc575,
  title = {Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images — arXiv:2608.12230},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.12230},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a06eb4abc575; sha256 6aa51f8a279bfb3f576c378ae273dfa4c13e09d027cdc7c97cbe26f72e6b4591},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.12230. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Non-destructive food quality assessment has increasingly benefited from hyperspectral imaging (HSI), which captures spectral signatures linked to biochemical changes during storage. Estimating day-wise freshness, however, remains challenging owing to strong inter-fillet variability and scarce labelled data per product. All existing deep learning approaches for HSI-based freshness prediction operate under full supervision, requiring densely annotated training sets that are costly to obtain at the individual-product level. We introduce, to the best of our knowledge, the first few-shot learning framework for HSI-based food quality estimation. Each fillet defines a distinct episodic task, and a CORAL-style ordinal prediction head captures the ranked nature of freshness progression through cumulative threshold modelling. Biologically grounded monotonicity and embedding smoothness constraints further guide predictions toward plausible trajectories. On a 16-day salmon HSI dataset under a strict unseen-fillet protocol, our method achieves a mean absolute error of 1.58 days and 2-day accuracy of 72.3% with only three labelled days per fillet, substantially outperforming scalar regression and label-distribution baselines under an identical unseen-fillet protocol.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-13 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.12230 — 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 2026-10-19T05:08:46.777Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).