DeepAI AI Chat
Log In Sign Up

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

by   Chunbo Lang, et al.

Recently few-shot segmentation (FSS) has been extensively developed. Most previous works strive to achieve generalization through the meta-learning framework derived from classification tasks; however, the trained models are biased towards the seen classes instead of being ideally class-agnostic, thus hindering the recognition of new concepts. This paper proposes a fresh and straightforward insight to alleviate the problem. Specifically, we apply an additional branch (base learner) to the conventional FSS model (meta learner) to explicitly identify the targets of base classes, i.e., the regions that do not need to be segmented. Then, the coarse results output by these two learners in parallel are adaptively integrated to yield precise segmentation prediction. Considering the sensitivity of meta learner, we further introduce an adjustment factor to estimate the scene differences between the input image pairs for facilitating the model ensemble forecasting. The substantial performance gains on PASCAL-5i and COCO-20i verify the effectiveness, and surprisingly, our versatile scheme sets a new state-of-the-art even with two plain learners. Moreover, in light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting, i.e., generalized FSS, where the pixels of both base and novel classes are required to be determined. The source code is available at


page 1

page 4

page 7

page 8


Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation

Few-shot segmentation aims to devise a generalizing model that segments ...

A Hybrid Approach with Optimization and Metric-based Meta-Learner for Few-Shot Learning

Few-shot learning aims to learn classifiers for new classes with only a ...

3D Meta-Segmentation Neural Network

Though deep learning methods have shown great success in 3D point cloud ...

Few-Shot Object Detection via Variational Feature Aggregation

As few-shot object detectors are often trained with abundant base sample...

A Strong Baseline for Generalized Few-Shot Semantic Segmentation

This paper introduces a generalized few-shot segmentation framework with...

Context-Aware Ensemble Learning for Time Series

We investigate ensemble methods for prediction in an online setting. Unl...

Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

Few-shot segmentation, which aims to segment unseen-class objects given ...

Code Repositories


Official PyTorch Implementation of Learning What Not to Segment: A New Perspective on Few-Shot Segmentation (CVPR 2022).

view repo