Multi-modal image segmentation

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (4 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Image Segmentation Using Text and Image Prompts
  18 Dec 2021 6 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.
• HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
  9 Apr 2018 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.
• MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentation
  9 Mar 2025 1 repository listed
  Meanwhile, to memorize the semantic knowledge, we propose a training-only Semantic Prototype Memory Module (SPMM) to store category-level prototypes across training for facilitating SAM2's transition from instance to…
• PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates
  20 Jul 2024 1 repository listed Syntology ran 15 of 16 samples · 1 unverified
  In this paper, we, for the first time, formulate such a challenging setting and propose Preference-Aware Self-diStillatION (PASSION) for incomplete multi-modal medical image segmentation under imbalanced missing rates.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.