pith. sign in

arxiv: 1506.04579 · v2 · pith:VFSPDF5Pnew · submitted 2015-06-15 · 💻 cs.CV

ParseNet: Looking Wider to See Better

Wei Liu , Andrew Rabinovich , Alexander C. Berg This is my paper
classification 💻 cs.CV
keywords approachperformancebaselinesfeatureglobalnetworksparsenetproposed
0
0 comments X
read the original abstract

We present a technique for adding global context to deep convolutional networks for semantic segmentation. The approach is simple, using the average feature for a layer to augment the features at each location. In addition, we study several idiosyncrasies of training, significantly increasing the performance of baseline networks (e.g. from FCN). When we add our proposed global feature, and a technique for learning normalization parameters, accuracy increases consistently even over our improved versions of the baselines. Our proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines, and near current state-of-the-art performance on PASCAL VOC 2012 semantic segmentation with a simple approach. Code is available at https://github.com/weiliu89/caffe/tree/fcn .

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Rethinking Atrous Convolution for Semantic Image Segmentation

    cs.CV 2017-06 unverdicted novelty 6.0

    DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF p...

  2. Attention-Mamba: A Mamba-Enhanced Multi-Scale Parallel Inference Network for Medical Image Segmentation

    cs.CV 2024-02 unverdicted novelty 5.0

    Attention-Mamba uses parallel branches, Recursive Alignment Module, and Mamba-enhanced attention to report highest segmentation accuracy on Synapse, ACDC, ISIC-2018, and PH2 with 14.05M parameters and 8.94 GFLOPs.

  3. Improving Semantic Segmentation via Dilated Affinity

    cs.CV 2019-07 unverdicted novelty 4.0

    Dilated affinity is jointly predicted with segmentation labels to strengthen features and support efficient label propagation refinement on benchmark datasets.

  4. Adaptive Context Encoding Module for Semantic Segmentation

    cs.CV 2019-07 unverdicted novelty 4.0

    Proposes ACE module with three deformable convolution blocks that outperforms PPM and ASPP on Pascal-Context and ADE20K datasets for semantic segmentation.

  5. Learning Where to Look While Tracking Instruments in Robot-assisted Surgery

    cs.CV 2019-06 unverdicted novelty 4.0

    An end-to-end multitask model with shared encoder, separate decoders, batch-Wasserstein loss, and soft attention module reports better performance than prior segmentation and saliency methods on the MICCAI robotic ins...