Hierarchical Material Recognition from Local Appearance
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Abstract (arXiv)
We introduce a taxonomy of materials for hierarchical recognition from local appearance. Our taxonomy is motivated by vision applications and is arranged according to the physical traits of materials. We contribute a diverse, in-the-wild dataset with images and depth maps of the taxonomy classes. Utilizing the taxonomy and dataset, we present a method for hierarchical material recognition based on graph attention networks. Our model leverages the taxonomic proximity between classes and achieves state-of-the-art performance. We demonstrate the model's potential to generalize to adverse, real-world imaging conditions, and that novel views rendered using the depth maps can enhance this capability. Finally, we show the model's capacity to rapidly learn new materials in a few-shot learning setting.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 32 of its 100 references that are in the map. First posted on arXiv 28 May 2025.
Builds on
32 of 100 references.
References · 32
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20 more
- 2018Rethinking Knowledge Graph Propagation for Zero-Shot Learningcited by 87
- 2014Material Recognition in the Wild with the Materials in Context Databasecited by 79
- 2016Deep TEN: Texture Encoding Networkcited by 44
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- 2024Lacunarity Pooling Layers for Plant Image Classification using Texture Analysiscited by 2
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
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