Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Swin Transformers Make Strong Contextual Encoders for VHR Image Road Extraction. Retrieved May 24, 2026, from https://4ort.xyz/entity/swin-transformers-make-strong-contextual-encoders-for-vhr-image-road-extraction
MLA“Swin Transformers Make Strong Contextual Encoders for VHR Image Road Extraction.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/swin-transformers-make-strong-contextual-encoders-for-vhr-image-road-extraction.
BibTeX@misc{4ortxyz_swin-transformers-make-strong-contextual-encoders-for-vhr-image-road-extraction_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Swin Transformers Make Strong Contextual Encoders for VHR Image Road Extraction}}, year = {2026}, url = {https://4ort.xyz/entity/swin-transformers-make-strong-contextual-encoders-for-vhr-image-road-extraction}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Swin Transformers Make Strong Contextual Encoders for VHR Image Road Extraction — https://4ort.xyz/entity/swin-transformers-make-strong-contextual-encoders-for-vhr-image-road-extraction (retrieved 2026-05-24)
