We propose a deep learning system for attention-guided dual-layer image
compression (AGDL). In the AGDL compression system, an image is encoded into
two layers, a base layer and an attention-guided refinement layer. Unlike the
existing ROI image compression methods that spend an extra bit budget equally
on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and
near a saliency sketch within ROI that are critical to perceptual quality. Only
the critical pixels are further sampled by compressive sensing (CS) to form a
very compact refinement layer. Another novel CNN method is developed to jointly
decode the two compression layers for a much refined reconstruction, while
strictly satisfying the transmitted CS constraints on perceptually critical
pixels. Extensive experiments demonstrate that the proposed AGDL system
advances the state of the art in perception-aware image compression.

提出了 AGDL，一种基于深度学习的双层图像压缩系统，用注意力机制识别感知关键像素，利用压缩感知技术进一步采样，进一步提升重构效果，并在感知感受度图像压缩领域取得了最新的进展。

深度重建压缩感知显著性骨架引导图像压缩

Attention-guided Image Compression by Deep Reconstruction of Compressive  Sensed Saliency Skeleton

The recently introduced BERT model exhibits strong performance on several
language understanding benchmarks. In this paper, we describe a simple
re-implementation of BERT for commonsense reasoning. We show that the
attentions produced by BERT can be directly utilized for tasks such as the
Pronoun Disambiguation Problem and Winograd Schema Challenge. Our proposed
attention-guided commonsense reasoning method is conceptually simple yet
empirically powerful. Experimental analysis on multiple datasets demonstrates
that our proposed system performs remarkably well on all cases while
outperforming the previously reported state of the art by a margin. While
results suggest that BERT seems to implicitly learn to establish complex
relationships between entities, solving commonsense reasoning tasks might
require more than unsupervised models learned from huge text corpora.

本文描述了 BERT 在常识推理任务中的简单再现，展示了 BERT 生成的关注力可以被直接用于 Pronoun Disambiguation Problem 和 Winograd Schema Challenge 这些任务，提出的基于关注力的常识推理方法在各种数据集上表现出色，超过了之前报道的最先进水平。

关注并非你需要的全部 —— 常识推理

Attention Is (not) All You Need for Commonsense Reasoning

Ghosting artifacts caused by moving objects or misalignments is a key
challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous
methods first register the input low dynamic range (LDR) images using optical
flow before merging them, which are error-prone and cause ghosts in results. A
very recent work tries to bypass optical flows via a deep network with
skip-connections, however, which still suffers from ghosting artifacts for
severe movement. To avoid the ghosting from the source, we propose a novel
attention-guided end-to-end deep neural network (AHDRNet) to produce
high-quality ghost-free HDR images. Unlike previous methods directly stacking
the LDR images or features for merging, we use attention modules to guide the
merging according to the reference image. The attention modules automatically
suppress undesired components caused by misalignments and saturation and
enhance desirable fine details in the non-reference images. In addition to the
attention model, we use dilated residual dense block (DRDB) to make full use of
the hierarchical features and increase the receptive field for hallucinating
the missing details. The proposed AHDRNet is a non-flow-based method, which can
also avoid the artifacts generated by optical-flow estimation error.
Experiments on different datasets show that the proposed AHDRNet can achieve
state-of-the-art quantitative and qualitative results.

提出了一种采用注意力模块引导合并 LDR 图像的高动态范围成像（AHDR）神经网络，此方法可避免光流估计误差引起的伪影问题，通过实验证明该方法能够在图像质量上达到最先进的水平。