會議論文
| 學年 | 111 |
|---|---|
| 學期 | 1 |
| 發表日期 | 2022-10-24 |
| 作品名稱 | Image Outpainting Based On Attention Model |
| 作品名稱（其他語言） | 基於注意模型的圖像擴展 |
| 著者 | Wei-Chien Tai; Shwu-Huey Yen; Yihjia Tsai |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | CICET 2022 Conference |
| 會議地點 | 新北市，臺灣 |
| 摘要 | Along the advanced progresses on deep neural networks, there are many impressive results on image inpainting. Consequently, several research are trying to transfer successful experiences into image outpainting. Contextual attention net is one of the popular architectural units being applied to outpainting. We argue that it may not as suitable when embedded in an outpainting network. Instead, we adopt SEnet for it has global receptive field and channel-wise feature recalibration. This is very helpful for image outpainting. We also propose a local discriminator mechanism to decide whether a randomly select partial image is a real one. By ‘randomness’, the generator can produce a realistic result. |
| 關鍵字 | image outpainting;image inpainting;attention module;Squeeze Excitation Network (SEnet);discriminator |
| 語言 | en_US |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 淡水校園 |
| 研討會時間 | 20221024~20221024 |
| 通訊作者 | |
| 國別 | JPN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126990 ) |