AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
arXiv preprint arXiv:2312.08874 , year=
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An LLM-integrated semantic framework for V2X claims a 33.54% average reduction in transmitted data volume in a multilane traffic simulation.
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AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation
AnchorSeg uses ordered query banks of latent reasoning tokens plus a spatial anchor token and a Token-Mask Cycle Consistency loss to achieve 67.7% gIoU and 68.1% cIoU on the ReasonSeg benchmark.
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Exploring LLM in Semantic Communication for V2X Networks
An LLM-integrated semantic framework for V2X claims a 33.54% average reduction in transmitted data volume in a multilane traffic simulation.