Where Does Texture Evidence Live in SAM? Features, Proposal Masks, and Texture Segmentation
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Abstract (arXiv)
Texture segmentation stresses foundation segmentation because meaningful regions are defined by material or repeated appearance rather than object identity. Segment Anything Models (SAMs) often fail by default on such texture-defined partitions, but this failure is ambiguous: the texture evidence may be absent, missing from the proposal bank, or present but selected or assembled incorrectly by an object-centric readout. We ask what texture-relevant evidence is already preserved in frozen SAM before adaptation. We study two frozen evidence spaces: multiscale features, probed with a minimal clustering readout, and the automatic proposal bank, treated as evidence for a supervised consolidation readout. SAM is frozen throughout; we do not fine-tune the backbone or retrain the proposal generator. Across RWTD, STLD, an ADE20K-selected refined-crop complement, and a ControlNet-stitched PTD bridge archive, frozen SAM is not a texture segmenter by default, but its failures are not simple texture blindness. Coarse frozen features preserve texture organization, and proposal banks often contain texture-aligned masks or fragments. Natural scenes more often require assembly and commitment over fragments, while cleaner synthetic cases more often reduce to selecting an already coherent proposal. Default mask failure should therefore be decomposed into representation evidence, proposal-bank support, readout mismatch, and commitment failure.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 7 Jun 2026.
Builds on
14 of 21 references.
References · 14
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2023Segment Anythingcited by 8,234
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
- 2013Describing Textures in the Wildcited by 2,125
- 2023Segment Anything in High Qualitycited by 376
- 2023Personalize Segment Anything Model with One Shotcited by 250
- 2014Material Recognition in the Wild with the Materials in Context Databasecited by 79
- 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
- 2018From BoW to CNN: Two Decades of Texture Representation for Texture Classificationcited by 40
- 2015Deep filter banks for texture recognition, description, and segmentationcited by 19
- 2024ASAM: Boosting Segment Anything Model with Adversarial Tuningcited by 12
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