Rethinking Convolutional Semantic Segmentation Learning

10/22/2017
by   Mrinal Haloi, et al.
0

Deep convolutional semantic segmentation (DCSS) learning doesn't converge to an optimal local minimum with random parameters initializations; a pre-trained model on the same domain becomes necessary to achieve convergence.In this work, we propose a joint cooperative end-to-end learning method for DCSS. It addresses many drawbacks with existing deep semantic segmentation learning; the proposed approach simultaneously learn both segmentation and classification; taking away the essential need of the pre-trained model for learning convergence. We present an improved inception based architecture with partial attention gating (PAG) over encoder information. The PAG also adds to achieve faster convergence and better accuracy for segmentation task. We will show the effectiveness of this learning on a diabetic retinopathy classification and segmentation dataset.

READ FULL TEXT
research
07/20/2023

Label Calibration for Semantic Segmentation Under Domain Shift

Performance of a pre-trained semantic segmentation model is likely to su...
research
08/22/2022

Prompt-Matched Semantic Segmentation

The objective of this work is to explore how to effectively and efficien...
research
09/14/2020

EfficientSeg: An Efficient Semantic Segmentation Network

Deep neural network training without pre-trained weights and few data is...
research
05/06/2023

Prompt What You Need: Enhancing Segmentation in Rainy Scenes with Anchor-based Prompting

Semantic segmentation in rainy scenes is a challenging task due to the c...
research
03/31/2021

Classification of Hematoma: Joint Learning of Semantic Segmentation and Classification

Cerebral hematoma grows rapidly in 6-24 hours and misprediction of the g...
research
06/09/2018

Robust Semantic Segmentation with Ladder-DenseNet Models

We present semantic segmentation experiments with a model capable to per...
research
11/13/2015

Learning Dense Convolutional Embeddings for Semantic Segmentation

This paper proposes a new deep convolutional neural network (DCNN) archi...

Please sign up or login with your details

Forgot password? Click here to reset