Real-time video segmentation is a crucial task for many real-world
applications such as autonomous driving and robot control. Since
state-of-the-art semantic segmentation models are often too heavy for real-time
applications despite their impressive performance, researchers have proposed
lightweight architectures with speed-accuracy trade-offs, achieving real-time
speed at the expense of reduced accuracy. In this paper, we propose a novel
framework to speed up any architecture with skip-connections for real-time
vision tasks by exploiting the temporal locality in videos. Specifically, at
the arrival of each frame, we transform the features from the previous frame to
reuse them at specific spatial bins. We then perform partial computation of the
backbone network on the regions of the current frame that captures temporal
differences between the current and previous frame. This is done by dynamically
dropping out residual blocks using a gating mechanism which decides which
blocks to drop based on inter-frame distortion. We validate our
Spatial-Temporal Mask Generator (STMG) on video semantic segmentation
benchmarks with multiple backbone networks, and show that our method largely
speeds up inference with minimal loss of accuracy.

本文提出了一个新的框架，利用视频中的时空局部性来加速实时视觉任务中带有跳跃连接的任何结构，通过基于失真的门控机制动态删除残差块来在当前帧的区域上执行骨干网络的部分计算。

适用于实时视频分割的失真感知网络剪枝和特征重用

Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation

Multi-person pose estimation methods generally follow top-down and bottom-up
paradigms, both of which can be considered as two-stage approaches thus leading
to the high computation cost and low efficiency. Towards a compact and
efficient pipeline for multi-person pose estimation task, in this paper, we
propose to represent the human parts as points and present a novel body
representation, which leverages an adaptive point set including the human
center and seven human-part related points to represent the human instance in a
more fine-grained manner. The novel representation is more capable of capturing
the various pose deformation and adaptively factorizes the long-range
center-to-joint displacement thus delivers a single-stage differentiable
network to more precisely regress multi-person pose, termed as AdaptivePose.
For inference, our proposed network eliminates the grouping as well as
refinements and only needs a single-step disentangling process to form
multi-person pose. Without any bells and whistles, we achieve the best
speed-accuracy trade-offs of 67.4% AP / 29.4 fps with DLA-34 and 71.3% AP / 9.1
fps with HRNet-W48 on COCO test-dev dataset.

本文提出了一种更加紧凑和高效的多人姿态估计框架，使用自适应点集来表示人体部位并使用单阶段网络精细地回归多人姿态，在 COCO 测试开发数据集上实现了最佳速度 - 准确度平衡。