Object Detection via Self-Supervised Instance Segmentation
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Solution Overview
Problem
Instance segmentation in computer vision is expensive and time-consuming due to the need for costly annotations like bounding boxes and segmentation masks, and existing methods are limited to a single type of object class, making them inefficient for diverse applications.
Innovation Solution
A self-supervised instance segmentation framework using a free mask generator and self-supervised instance segmentor that generates coarse object masks from unlabeled images, allowing for unsupervised learning and refining mask quality through self-training, eliminating the need for manual annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotations (bounding boxes and segmentation masks) are used for training instance segmentation models, then training data quality is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system uses automated self-training where the instance segmentation model generates its own training annotations by processing unlabeled images through a free mask generator and self-training mechanism, eliminating the need for manual human annotation while progressively improving annotation quality through iterative refinement
Solution Approach 2:
A free mask generator serves as an intermediary component that produces coarse mask predictions from unlabeled images, which then serve as initial training annotations for the instance segmentation model, bridging the gap between unlabeled data and high-quality annotations
2Measurement precision
If expert annotators are used to generate training annotations, then annotation accuracy is improved, but cost increases
Solution Approach 1:
The system replaces expensive expert annotators with an automated self-training pipeline where the model generates its own training data through free mask generation and iterative refinement, achieving high annotation accuracy without human intervention
Solution Approach 2:
The system creates synthetic training annotations by copying and refining mask predictions from unlabeled images through the self-training process, producing annotation-quality data without requiring actual human expert input
3Productivity
If training data is limited to a single object class, then training efficiency is improved, but adaptability decreases
Solution Approach 1:
The self-training framework is designed to be universally applicable to multiple object classes simultaneously. The free mask generator and instance segmentation model can process diverse object types (pedestrians, vehicles, animals, etc.) without requiring class-specific training configurations, enabling one system to handle many different object categories
Solution Approach 2:
The system performs preliminary processing of unlabeled images through the free mask generator to create class-agnostic coarse masks before instance segmentation. This preliminary step prepares data that can be adapted to any object class, allowing the model to efficiently train on multiple classes without requiring class-specific pre-processing
Data Source
AI summary
Apparatuses, systems, and techniques are presented to detect one or more objects in one or more images. In at least one embodiment, one or more neural networks can be trained to detect one or more objects, in one or more unlabeled images, based at least in part upon one or more predicted segmentations of the one or more objects.