Monocular depth estimation is a fundamental task in computer vision and has
drawn increasing attention. Recently, some methods reformulate it as a
classification-regression task to boost the model performance, where continuous
depth is estimated via a linear combination of predicted probability
distributions and discrete bins. In this paper, we present a novel framework
called BinsFormer, tailored for the classification-regression-based depth
estimation. It mainly focuses on two crucial components in the specific task:
1) proper generation of adaptive bins and 2) sufficient interaction between
probability distribution and bins predictions. To specify, we employ the
Transformer decoder to generate bins, novelly viewing it as a direct set-to-set
prediction problem. We further integrate a multi-scale decoder structure to
achieve a comprehensive understanding of spatial geometry information and
estimate depth maps in a coarse-to-fine manner. Moreover, an extra scene
understanding query is proposed to improve the estimation accuracy, which turns
out that models can implicitly learn useful information from an auxiliary
environment classification task. Extensive experiments on the KITTI, NYU, and
SUN RGB-D datasets demonstrate that BinsFormer surpasses state-of-the-art
monocular depth estimation methods with prominent margins. Code and pretrained
models will be made publicly available at
https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox.

本文提出了一种适用于基于分类 - 回归的深度估算任务的新框架 BinsFormer，该框架主要关注该任务中的两个关键组件：适应性 bins 的恰当生成和概率分布及 bins 预测之间的足够交互。

BinsFormer: 重温自适应分组用于单目深度估计

BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

High-quality image inpainting requires filling missing regions in a damaged
image with plausible content. Existing works either fill the regions by copying
image patches or generating semantically-coherent patches from region context,
while neglect the fact that both visual and semantic plausibility are
highly-demanded. In this paper, we propose a Pyramid-context ENcoder Network
(PEN-Net) for image inpainting by deep generative models. The PEN-Net is built
upon a U-Net structure, which can restore an image by encoding contextual
semantics from full resolution input, and decoding the learned semantic
features back into images. Specifically, we propose a pyramid-context encoder,
which progressively learns region affinity by attention from a high-level
semantic feature map and transfers the learned attention to the previous
low-level feature map. As the missing content can be filled by attention
transfer from deep to shallow in a pyramid fashion, both visual and semantic
coherence for image inpainting can be ensured. We further propose a multi-scale
decoder with deeply-supervised pyramid losses and an adversarial loss. Such a
design not only results in fast convergence in training, but more realistic
results in testing. Extensive experiments on various datasets show the superior
performance of the proposed network

本文提出了一种基于 U-Net 结构的金字塔上下文编码网络 (PEN-Net)，并且使用深度生成模型实现图像修复，并通过逐层传递学习到的注意力，使得修复图像在视觉和语义上具有一致性和真实感。