Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation

11/21/2022
by   Zhen Zhao, et al.
0

Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data. However, most existing studies treat all unlabeled data equally and barely consider the differences and training difficulties among unlabeled instances. Differentiating unlabeled instances can promote instance-specific supervision to adapt to the model's evolution dynamically. In this paper, we emphasize the cruciality of instance differences and propose an instance-specific and model-adaptive supervision for semi-supervised semantic segmentation, named iMAS. Relying on the model's performance, iMAS employs a class-weighted symmetric intersection-over-union to evaluate quantitative hardness of each unlabeled instance and supervises the training on unlabeled data in a model-adaptive manner. Specifically, iMAS learns from unlabeled instances progressively by weighing their corresponding consistency losses based on the evaluated hardness. Besides, iMAS dynamically adjusts the augmentation for each instance such that the distortion degree of augmented instances is adapted to the model's generalization capability across the training course. Not integrating additional losses and training procedures, iMAS can obtain remarkable performance gains against current state-of-the-art approaches on segmentation benchmarks under different semi-supervised partition protocols.

READ FULL TEXT

page 3

page 8

research
03/21/2023

CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation learns a model for classifying pix...
research
12/09/2022

Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation

Recent studies on semi-supervised semantic segmentation (SSS) have seen ...
research
08/28/2023

Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation

Visual bird's eye view (BEV) semantic segmentation helps autonomous vehi...
research
10/11/2021

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning

Due to the limited and even imbalanced data, semi-supervised semantic se...
research
04/10/2022

ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles

We present a semi-supervised method for panoptic segmentation based on C...
research
04/22/2021

Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance

In order to leverage and profit from unlabelled data, semi-supervised fr...
research
12/04/2018

Multiview Cross-supervision for Semantic Segmentation

This paper presents a semi-supervised learning framework for a customize...

Please sign up or login with your details

Forgot password? Click here to reset