Currently, image-denoising methods based on deep learning cannot adequately
reconcile contextual semantic information and spatial details. To take these
information optimizations into consideration, in this paper, we propose a
Context-Space Progressive Collaborative Network (CS-PCN) for image denoising.
CS-PCN is a multi-stage hierarchical architecture composed of a context mining
siamese sub-network (CM2S) and a space synthesis sub-network (3S). CM2S aims at
extracting rich multi-scale contextual information by sequentially connecting
multi-layer feature processors (MLFP) for semantic information pre-processing,
attention encoder-decoders (AED) for multi-scale information, and multi-conv
attention controllers (MCAC) for supervised feature fusion. 3S parallels MLFP
and a single-scale cascading block to learn image details, which not only
maintains the contextual information but also emphasizes the complementary
spatial ones. Experimental results show that CS-PCN achieves significant
performance improvement in synthetic and real-world noise removal.

本文介绍了一种基于 Context-Space Progressive Collaborative Network (CS-PCN) 的图像去噪方法，该方法可以有效地处理语义信息和空间细节，并通过层次化的架构进行多阶段处理，具有较好的性能表现。

CS-PCN: 图像去噪的上下文空间逐步协作网络

CS-PCN: Context-Space Progressive Collaborative Network for Image  Denoising

Language representation models such as BERT could effectively capture
contextual semantic information from plain text, and have been proved to
achieve promising results in lots of downstream NLP tasks with appropriate
fine-tuning. However, most existing language representation models cannot
explicitly handle coreference, which is essential to the coherent understanding
of the whole discourse. To address this issue, we present CorefBERT, a novel
language representation model that can capture the coreferential relations in
context. The experimental results show that, compared with existing baseline
models, CorefBERT can achieve significant improvements consistently on various
downstream NLP tasks that require coreferential reasoning, while maintaining
comparable performance to previous models on other common NLP tasks. The source
code and experiment details of this paper can be obtained from
this https URL

本文提出了一种新的语言表征模型 CorefBERT，可以在捕获上下文中的语义信息的同时，明确处理代词消解，实现语篇的一致理解，并在各种需要代词推理的下游 NLP 任务中获得显著的改善。