Disentangle then Parse Night-time Semantic Segmentation with Illumination Disentanglement
University of Science and Technology of China Shanghai AI Laboratory
SED A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation
研究背景： 传统的方法只能分割训练集的种类，不能识别出来在训练集中没有的未知场景，同时两阶段和单阶段的方法都存在不足。两阶段的框架存在不足：计算效率低，没有充分利用上下文信息；单阶段的框架存在不足：对于低分辨率的输入，主干网络对空间信息变得不敏感，即使加入额外的网络来提供空间信息，也会增加计算资源，分割种类的增加也会增加计算资源。
High Quality Segmentation for Ultra High-resolution Images
**摘要：**To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch crop- ping, and cascade model, cannot address well the balance issue between accuracy and computation cost. Motivated by the fact that humans distinguish among objects continu- ously from coarse to precise levels, we propose the Contin- uous Refinement Model (CRM) for the ultra high-resolution segmentation refinement task. CRM continuously aligns the feature map with the refinement target and aggregates fea- tures to reconstruct these image details. Besides, our CRM shows its significant generalization ability to fill the resolu- tion gap between low-resolution training images and ultra high-resolution testing ones. We present quantitative per- formance evaluation and visualization to show that our pro- posed method is fast and effective on image segmentation refinement. Code is available at https://github.com/dvlab-research/Entity/tree/main/CRM.