Incremental Few-Shot Instance Segmentation

05/11/2021
by   Dan Andrei Ganea, et al.
0

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly.

READ FULL TEXT
research
07/20/2018

Bounding Box Embedding for Single Shot Person Instance Segmentation

We present a bottom-up approach for the task of object instance segmenta...
research
09/29/2019

PolarMask: Single Shot Instance Segmentation with Polar Representation

In this paper, we introduce an anchor-box free and single shot instance ...
research
01/03/2023

Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation

Few Shot Instance Segmentation (FSIS) requires models to detect and segm...
research
04/15/2023

Instance-level Few-shot Learning with Class Hierarchy Mining

Few-shot learning is proposed to tackle the problem of scarce training d...
research
07/07/2022

Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners

Few-shot recognition involves training an image classifier to distinguis...
research
12/03/2020

Single-shot Path Integrated Panoptic Segmentation

Panoptic segmentation, which is a novel task of unifying instance segmen...
research
10/03/2022

Learning Equivariant Segmentation with Instance-Unique Querying

Prevalent state-of-the-art instance segmentation methods fall into a que...

Please sign up or login with your details

Forgot password? Click here to reset