Natural image matting is a fundamental and challenging computer vision task.
It has many applications in image editing and composition. Recently, deep
learning-based approaches have achieved great improvements in image matting.
However, most of them require a user-supplied trimap as an auxiliary input,
which limits the matting applications in the real world. Although some
trimap-free approaches have been proposed, the matting quality is still
unsatisfactory compared to trimap-based ones. Without the trimap guidance, the
matting models suffer from foreground-background ambiguity easily, and also
generate blurry details in the transition area. In this work, we propose
PP-Matting, a trimap-free architecture that can achieve high-accuracy natural
image matting. Our method applies a high-resolution detail branch (HRDB) that
extracts fine-grained details of the foreground with keeping feature resolution
unchanged. Also, we propose a semantic context branch (SCB) that adopts a
semantic segmentation subtask. It prevents the detail prediction from local
ambiguity caused by semantic context missing. In addition, we conduct extensive
experiments on two well-known benchmarks: Composition-1k and Distinctions-646.
The results demonstrate the superiority of PP-Matting over previous methods.
Furthermore, we provide a qualitative evaluation of our method on human matting
which shows its outstanding performance in the practical application. The code
and pre-trained models will be available at PaddleSeg:
this https URL.

本文提出了一种名为 PP-Matting 的无 TriMap 自然图像抠图架构，可以实现高精度的自然图像抠图，利用高清分支和语义上下文分支防止前景背景歧义和局部不确定性，与现有方法相比表现优异。

PP-Matting：高精度自然图像抠图

PP-Matting: High-Accuracy Natural Image Matting

Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring
(TR), has been widely studied for deep neural network (DNN) model compression,
especially for recurrent neural networks (RNNs). However, compressing
convolutional neural networks (CNNs) using TT/TR always suffers significant
accuracy loss. In this paper, we propose a systematic framework for tensor
decomposition-based model compression using Alternating Direction Method of
Multipliers (ADMM). By formulating TT decomposition-based model compression to
an optimization problem with constraints on tensor ranks, we leverage ADMM
technique to systemically solve this optimization problem in an iterative way.
During this procedure, the entire DNN model is trained in the original
structure instead of TT format, but gradually enjoys the desired low tensor
rank characteristics. We then decompose this uncompressed model to TT format
and fine-tune it to finally obtain a high-accuracy TT-format DNN model. Our
framework is very general, and it works for both CNNs and RNNs, and can be
easily modified to fit other tensor decomposition approaches. We evaluate our
proposed framework on different DNN models for image classification and video
recognition tasks. Experimental results show that our ADMM-based TT-format
models demonstrate very high compression performance with high accuracy.
Notably, on CIFAR-100, with 2.3X and 2.4X compression ratios, our models have
1.96% and 2.21% higher top-1 accuracy than the original ResNet-20 and
ResNet-32, respectively. For compressing ResNet-18 on ImageNet, our model
achieves 2.47X FLOPs reduction without accuracy loss.

本文提出一种基于张量分解的模型压缩系统，采用交替方向乘子法进行优化求解。该系统适用于卷积神经网络和循环神经网络，可大幅降低计算量和参数量，并保持较高的准确性。