The devil is in the details: Window-based attention for image compression | |
Renjie Zou1; Chunfeng Song1; Zhaoxiang Zhang1,2 | |
2022 | |
会议名称 | IEEE/CVF Conference on Computer Vision and Pattern Recognition |
会议日期 | 2022-06-19 |
会议地点 | New Orleans Louisiana, USA |
摘要 | Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the non-repetitive textures, which severely affects the reconstruction quality. Therefore, how to make full use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of Vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://github. com/Googolxx/STF. |
收录类别 | EI |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 多模态协同认知 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51616 |
专题 | 模式识别实验室 |
通讯作者 | Zhaoxiang Zhang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences (CASIA), UCAS 2.Centre for Artificial Intelligence and Robotics, HKISI_CAS |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Renjie Zou,Chunfeng Song,Zhaoxiang Zhang. The devil is in the details: Window-based attention for image compression[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Zou_The_Devil_Is_in_(2608KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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