A high-resolution network exhibits remarkable capability in extracting
multi-scale features for human pose estimation, but fails to capture long-range
interactions between joints and has high computational complexity. To address
these problems, we present a Dynamic lightweight High-Resolution Network
(Dite-HRNet), which can efficiently extract multi-scale contextual information
and model long-range spatial dependency for human pose estimation.
Specifically, we propose two methods, dynamic split convolution and adaptive
context modeling, and embed them into two novel lightweight blocks, which are
named dynamic multi-scale context block and dynamic global context block. These
two blocks, as the basic component units of our Dite-HRNet, are specially
designed for the high-resolution networks to make full use of the parallel
multi-resolution architecture. Experimental results show that the proposed
network achieves superior performance on both COCO and MPII human pose
estimation datasets, surpassing the state-of-the-art lightweight networks. Code
is available at: this https URL

通过在高分辨率网络中引入动态轻量化、多尺度上下文信息提取、长距离空间依赖建模的方法，本文提出了一种动态轻量化高分辨率网络模型（Dite-HRNet），该模型在 COCO 和 MPII 人体姿态估计数据集上取得了优异的表现，超越了现有的轻量化网络模型。

Dite-HRNet：用于人体姿态估计的动态轻量级高分辨率网络

Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose  Estimation

Modern handheld devices can acquire burst image sequence in a quick
succession. However, the individual acquired frames suffer from multiple
degradations and are misaligned due to camera shake and object motions. The
goal of Burst Image Restoration is to effectively combine complimentary cues
across multiple burst frames to generate high-quality outputs. Towards this
goal, we develop a novel approach by solely focusing on the effective
information exchange between burst frames, such that the degradations get
filtered out while the actual scene details are preserved and enhanced. Our
central idea is to create a set of pseudo-burst features that combine
complementary information from all the input burst frames to seamlessly
exchange information. However, the pseudo-burst cannot be successfully created
unless the individual burst frames are properly aligned to discount inter-frame
movements. Therefore, our approach initially extracts pre-processed features
from each burst frame and matches them using an edge-boosting burst alignment
module. The pseudo-burst features are then created and enriched using
multi-scale contextual information. Our final step is to adaptively aggregate
information from the pseudo-burst features to progressively increase resolution
in multiple stages while merging the pseudo-burst features. In comparison to
existing works that usually follow a late fusion scheme with single-stage
upsampling, our approach performs favorably, delivering state-of-the-art
performance on burst superresolution, burst low-light image enhancement, and
burst denoising tasks. The source code and pre-trained models are available at
https://github.com/akshaydudhane16/BIPNet.

该研究提出了一种新颖的 Burst Image Restoration 方法，使用伪爆发特征相互交换信息，通过快速匹配、多尺度的上下文信息和自适应聚合，还原高质量图像。与现有方法相比，该方法表现出最先进的性能，可用于爆发超分辨率、爆发低光图像增强和爆发去噪任务。