1.Yangzhou university mid-defend
2.try different improvements to inception(CABM,SE,SK)
1.Yangzhou university mid-defend
2.try different improvements to inception(CABM,SE,SK)
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1.chang the format of FLICK30
2.make the model with the FLICK30 dateset, the model is running now
1.recurrent inception+Transformer .
2.Research on encoder how to make innovation on inception or FCNN.
1.CBAM+CNN实现
2. Make a framework of FCNN+Tranformer
1.Read paper: 视觉问答中的多模态信息融合_庞章阳.pdf
2.recurrent "pytorch-tutorial-image-caption "
3.make a demo on RNN、 LSTM
1.read paper:UNITER:UNIVERSAL IMAGE-TEXT REPRESENT LEARNING
https://mountain.elcs.kyutech.ac.jp/gao/read-paper/-/blob/master/UNITER_UNiversal_Image-TExt.pdf
2.read paper:
Show and Tell: Lessons learned from the 2015
MSCOCO Image Captioning Challenge
3.recurrent “2”
3.learn the code of VL-BERT
1.vl-bert-codeを研究
1.pytochを勉強する
2.論文を読む:Unifying_Vision-and-Language_Tasks_via_Text_Generation.pdf(https://mountain.elcs.kyutech.ac.jp/gao/read-paper/-/blob/master/Unifying_Vision-and-Language_Tasks_via_Text_Generation.pdf)
3.VL-bert 実行に成功しました