| Image Outpainting Based On Attention Model | |
|---|---|
| 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） | |
| 著者 | Wei-Chien Tai; Shwu-Huey Yen; Yihjia Tsai |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | CICET'22: International Conference on Recent Advancements in Computing in AI, IoT and Computer Engineering Technology |
| 會議地點 | 新北市，台灣 |
| 摘要 | Along the advanced progresses on deep neural networks, there are many impressive results on image inpainting. Several research works have tried to transfer successful experiences into image outpainting. Contextual attention net is one of the popular architectural units being applied to inpainting. We argue that it may be not 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 non-fix local discriminator mechanism to decide whether a randomly select partial image is a real one. By ‘randomness’, the generator can produce a more realistic result. The experimental results are satisfactory and compatible to those on existing state-of-the-arts methods. |
| 關鍵字 | Image Outpainting;Image Inpainting;Non-Fix Local Discriminator;Attention Module;Squeeze Excitation Network (SENet) |
| 語言 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20221024~20221026 |
| 通訊作者 | |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | Proceedings of CICET 2022 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/123443 ) |
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