Image-guided depth completion aims at generating a dense depth map from
sparse LiDAR data and RGB image. Recent methods have shown promising
performance by reformulating it as a classification problem with two sub-tasks:
depth discretization and probability prediction. They divide the depth range
into several discrete depth values as depth categories, serving as priors for
scene depth distributions. However, previous depth discretization methods are
easy to be impacted by depth distribution variations across different scenes,
resulting in suboptimal scene depth distribution priors. To address the above
problem, we propose a progressive depth decoupling and modulating network,
which incrementally decouples the depth range into bins and adaptively
generates multi-scale dense depth maps in multiple stages. Specifically, we
first design a Bins Initializing Module (BIM) to construct the seed bins by
exploring the depth distribution information within a sparse depth map,
adapting variations of depth distribution. Then, we devise an incremental depth
decoupling branch to progressively refine the depth distribution information
from global to local. Meanwhile, an adaptive depth modulating branch is
developed to progressively improve the probability representation from
coarse-grained to fine-grained. And the bi-directional information interactions
are proposed to strengthen the information interaction between those two
branches (sub-tasks) for promoting information complementation in each branch.
Further, we introduce a multi-scale supervision mechanism to learn the depth
distribution information in latent features and enhance the adaptation
capability across different scenes. Experimental results on public datasets
demonstrate that our method outperforms the state-of-the-art methods. The code
will be open-sourced at [this https URL](this https URL).

提出了一种渐进的深度解耦和调制网络，通过将深度范围分解为多个范围，并自适应地生成多尺度稠密深度图，以解决现有深度离散化方法在不同场景中受深度分布变化影响导致子优化场景深度分布先验的问题。

灵活深度完成的渐进解耦与调制

Progressive Depth Decoupling and Modulating for Flexible Depth  Completion

Noisy label problems are inevitably in existence within medical image
segmentation causing severe performance degradation. Previous segmentation
methods for noisy label problems only utilize a single image while the
potential of leveraging the correlation between images has been overlooked.
Especially for video segmentation, adjacent frames contain rich contextual
information beneficial in cognizing noisy labels. Based on two insights, we
propose a Multi-Scale Temporal Feature Affinity Learning (MS-TFAL) framework to
resolve noisy-labeled medical video segmentation issues. First, we argue the
sequential prior of videos is an effective reference, i.e., pixel-level
features from adjacent frames are close in distance for the same class and far
in distance otherwise. Therefore, Temporal Feature Affinity Learning (TFAL) is
devised to indicate possible noisy labels by evaluating the affinity between
pixels in two adjacent frames. We also notice that the noise distribution
exhibits considerable variations across video, image, and pixel levels. In this
way, we introduce Multi-Scale Supervision (MSS) to supervise the network from
three different perspectives by re-weighting and refining the samples. This
design enables the network to concentrate on clean samples in a coarse-to-fine
manner. Experiments with both synthetic and real-world label noise demonstrate
that our method outperforms recent state-of-the-art robust segmentation
approaches. Code is available at this https URL

提出了一种基于多尺度时间特征亲和力学习框架的解决医学视频分割中存在的噪声标签问题的方法，在人工合成和真实世界标签噪声的实验中表现出优异的性能。

使用序列先验校正噪声标签：多尺度时间特征亲和力学习，实现强韧性视频分割

Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal  Feature Affinity Learning for Robust Video Segmentation

In this paper, we address the problem of estimating the positions of human
joints, i.e., articulated pose estimation. Recent state-of-the-art solutions
model two key issues, joint detection and spatial configuration refinement,
together using convolutional neural networks. Our work mainly focuses on
spatial configuration refinement by reducing variations of human poses
statistically, which is motivated by the observation that the scattered
distribution of the relative locations of joints e.g., the left wrist is
distributed nearly uniformly in a circular area around the left shoulder) makes
the learning of convolutional spatial models hard. We present a two-stage
normalization scheme, human body normalization and limb normalization, to make
the distribution of the relative joint locations compact, resulting in easier
learning of convolutional spatial models and more accurate pose estimation. In
addition, our empirical results show that incorporating multi-scale supervision
and multi-scale fusion into the joint detection network is beneficial.
Experiment results demonstrate that our method consistently outperforms
state-of-the-art methods on the benchmarks.

本文解决了人体关节位置估计的问题，提出了基于卷积神经网络的空间姿态模型优化方法，包括人体细节归一化，肢体归一化，以及多尺度监督和融合。实验表明，该方法在基准测试中表现优于现有的最佳方法。