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Segmenting Objects From Relational Visual Data.

Xiankai Lu, Wenguan Wang, Jianbing Shen

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 28, 2021

Summary

This summary is machine-generated.

An Attentive Graph Neural Network (AGNN) unifies object segmentation tasks like automatic video segmentation (AVS), image co-segmentation (ICS), and few-shot semantic segmentation (FSS). This model uses iterative information fusion over data graphs for accurate object discovery and segmentation.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Pixelwise object segmentation is crucial for understanding visual data.
Existing methods often address tasks like automatic video segmentation (AVS), image co-segmentation (ICS), and few-shot semantic segmentation (FSS) in isolation.
A unified approach is needed to handle diverse segmentation challenges from relational visual data.

Purpose of the Study:

To propose a unified framework for modeling various pixelwise object segmentation tasks.
To introduce an Attentive Graph Neural Network (AGNN) for holistic object segmentation.
To demonstrate the efficacy of AGNN in segmenting objects from relational visual data.

Main Methods:

Formulating segmentation tasks as iterative information fusion over data graphs.
Constructing a fully-connected graph where visual data instances are nodes and relations are edges.
Employing a differentiable attention mechanism to capture fine-grained semantic similarities between data instances.
Utilizing parametric message passing within the graph neural network.

Main Results:

AGNN successfully performs automatic video segmentation by highlighting foreground objects.
AGNN effectively extracts common objects in image co-segmentation tasks from noisy, related images.
AGNN demonstrates generalization capabilities for few-shot semantic segmentation with limited annotated data.
The model achieves accurate object discovery and segmentation across diverse tasks.

Conclusions:

AGNN provides a powerful and unified tool for pixelwise object pattern understanding.
The proposed method effectively leverages relational visual data for improved segmentation.
AGNN's ability to generalize across AVS, ICS, and FSS highlights its versatility.