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C$^2$-Routing: Complementary Cropping and Compression for Efficient Vision-Language Inference

By adminNovember 11, 20260

arXiv:2611.14469v1 Announce Type: cross Abstract: Visual Question Answering (VQA) imposes substantial computational overhead on vision-language models (VLMs), motivating model-agnostic input…

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C$^2$-Routing: Complementary Cropping and Compression for Efficient Vision-Language Inference

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