关于专访 vivo 总裁胡柏山，以下几个关键信息值得重点关注。本文结合最新行业数据和专家观点，为您系统梳理核心要点。
首先，Several open-source multimodal language models have adapted their methodologies accordingly, e.g., Gemma3 (opens in new tab) uses pan-and-scan and NVILA (opens in new tab) uses Dynamic S2. However, their trade-offs are difficult to understand across different datasets and hyperparameters. To this end, we conducted an ablation study of several techniques. We trained a smaller 5 billion parameter Phi-4 based proxy model on a dataset of 10 million image-text pairs, primarily composed of computer-use and GUI grounding data. We compared with Dynamic S2, which resizes images to a rectangular resolution that minimizes distortion while admitting a tiling by 384×384 squares; Multi-crop, which splits the image into potentially overlapping 384×384 squares and concatenates their encoded features on the token dimension; Multi-crop with S2, which broadens the receptive field by cropping into 1536×1536 squares before applying S2; and Dynamic resolution using the Naflex variant of SigLIP-2, a natively dynamic-resolution encoder with adjustable patch counts.
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其次，Together, these two structural opportunities form an epic chance in the AI era—one where the To B space far surpasses To C: on one side, the boundless expansion of digital labor; on the other, the full release of human value. Tokens become the unit of value measurement, circulation, and distribution running through it all, jointly propping up an entirely new economic form for the AI age.
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展望未来，专访 vivo 总裁胡柏山的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。