论文标题
淡出：将解码器和编码器的资产融合以进行任务不合时宜的UPPRAPLING
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
论文作者
论文摘要
我们考虑任务不合时宜的功能在密集预测中提升采样的问题，在该预测中，需要进行更新的操作员来促进对区域敏感的任务，例如语义细分和详细信息敏感的任务，例如图像垫子。现有的Up采样运算符通常可以在任何一种任务中都可以很好地工作，但两者兼而有之。在这项工作中，我们介绍了淡出的淡入淡出，插件和任务不合时宜的Upplampring操作员。淡出从三个设计选择中受益：i）考虑编码器和解码器特征在增加内核时代共同采样； ii）有效的半换档卷积运算符，可以颗粒控制每个特征点如何有助于上采样内核； iii）依赖解码器的门控机制，可增强细节描述。我们首先研究了淡出在玩具数据上的升采样属性，然后在大规模的语义分割和图像垫子上对其进行评估。尤其是，淡出通过在不同任务中持续优于最近的动态上取样操作员，从而揭示了其有效性和任务不足的特征。它还可以很好地跨越卷积和变压器架构，而计算开销很少。我们的工作还提供了关于使任务不合时宜的提升的深入见解。代码可在以下网址找到：http：//lnkiy.in/fade_in
We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. FADE benefits from three design choices: i) considering encoder and decoder features jointly in upsampling kernel generation; ii) an efficient semi-shift convolutional operator that enables granular control over how each feature point contributes to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced detail delineation. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting. In particular, FADE reveals its effectiveness and task-agnostic characteristic by consistently outperforming recent dynamic upsampling operators in different tasks. It also generalizes well across convolutional and transformer architectures with little computational overhead. Our work additionally provides thoughtful insights on what makes for task-agnostic upsampling. Code is available at: http://lnkiy.in/fade_in
