The objective of this work is to segment high-resolution images without
overloading GPU memory usage or losing the fine details in the output
segmentation map. The memory constraint means that we must either downsample
the big image or divide the image into local patches for separate processing.
However, the former approach would lose the fine details, while the latter can
be ambiguous due to the lack of a global picture. In this work, we present
MagNet, a multi-scale framework that resolves local ambiguity by looking at the
image at multiple magnification levels. MagNet has multiple processing stages,
where each stage corresponds to a magnification level, and the output of one
stage is fed into the next stage for coarse-to-fine information propagation.
Each stage analyzes the image at a higher resolution than the previous stage,
recovering the previously lost details due to the lossy downsampling step, and
the segmentation output is progressively refined through the processing stages.
Experiments on three high-resolution datasets of urban views, aerial scenes,
and medical images show that MagNet consistently outperforms the
state-of-the-art methods by a significant margin.

该文章提出了一个名为 MagNet 的多尺度框架，可以在不超载 GPU 内存使用或丢失输出细节的情况下对高分辨率图像进行分割。

渐进式语义分割

Progressive Semantic Segmentation

Popular deep learning frameworks require users to fine-tune their memory
usage so that the training data of a deep neural network (DNN) fits within the
GPU physical memory. Prior work tries to address this restriction by
virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be
utilized for memory allocations. Despite its merits, virtualizing memory can
incur significant performance overheads when the time needed to copy data back
and forth from CPU memory is higher than the latency to perform the
computations required for DNN forward and backward propagation. We introduce a
high-performance virtualization strategy based on a "compressing DMA engine"
(cDMA) that drastically reduces the size of the data structures that are
targeted for CPU-side allocations. The cDMA engine offers an average 2.6x
(maximum 13.8x) compression ratio by exploiting the sparsity inherent in
offloaded data, improving the performance of virtualized DNNs by an average 32%
(maximum 61%).

本文介绍了一种基于压缩 DMA 引擎的高性能虚拟化策略，通过利用离线数据中的稀疏性，提高了虚拟 DNN 的性能，平均性能提升 32％(最大可达 61％)。