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→ 后续文字；可变长度输出；图像—描述；图像—问题—答案；2013；DeViSE；视觉—语义空间；2014；LRCN / Show and Tell；图像描述；2015；区域—句子对齐 / VQA；局部对应与问题条件；2016；DCC；未配对类别的描述；Trevor Darrell；MIT 教职 1999—2008 → Berkeley，2008；R-CNN、FCN、LRCN 的共同作者之一；积累汇合，让新问题可训练：；说出内容 · 对应局部 · 按问题回答；年份标示预印本或会议窗口；技术联系不等于私人动机或单一研究者的谱系。\n\n视觉预训练、文字序列预测与配对数据三条积累进入2013至2016的研究时间带；下方提供Trevor Darrell公开研究背景。\n\n- 可复用视觉特征\n- 文字序列预测\n- 图文数据与共同评价\n- 分类预训练\n- 区域表示\n- 已有文字 → 后续文字\n- 可变长度输出\n- 图像—描述\n- 图像—问题—答案\n- 2013\n- DeViSE\n- 视觉—语义空间\n- 2014\n- LRCN / Show and Tell\n- 图像描述\n- 2015\n- 区域—句子对齐 / VQA\n- 局部对应与问题条件\n- 2016\n- DCC\n- 未配对类别的描述\n- Trevor Darrell\n- MIT 教职 1999—2008 → Berkeley，2008\n- R-CNN、FCN、LRCN 的共同作者之一\n- 积累汇合，让新问题可训练：\n- 说出内容 · 对应局部 · 按问题回答\n- 年份标示预印本或会议窗口；技术联系不等于私人动机或单一研究者的谱系。视觉预训练、文字序列预测与配对数据三条积累进入2013至2016的研究时间带；下方提供Trevor Darrell公开研究背景。 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