Anno-incomplete Multi-dataset Detection

TL;DR

This paper introduces a novel multi-dataset object detection approach that handles annotation incompleteness and heterogeneity, improving detection accuracy across multiple datasets.

Contribution

It proposes an end-to-end multi-task learning architecture with an attention feature extractor and knowledge amalgamation strategy for multi-dataset detection.

Findings

Achieves 2.17% mAP improvement on COCO

Achieves 2.10% mAP improvement on VOC

Effectively handles annotation incompleteness and heterogeneity

Abstract

Object detectors have shown outstanding performance on various public datasets. However, annotating a new dataset for a new task is usually unavoidable in real, since 1) a single existing dataset usually does not contain all object categories needed; 2) using multiple datasets usually suffers from annotation incompletion and heterogeneous features. We propose a novel problem as "Annotation-incomplete Multi-dataset Detection", and develop an end-to-end multi-task learning architecture which can accurately detect all the object categories with multiple partially annotated datasets. Specifically, we propose an attention feature extractor which helps to mine the relations among different datasets. Besides, a knowledge amalgamation training strategy is incorporated to accommodate heterogeneous features from different sources. Extensive experiments on different object detection datasets demonstrate the effectiveness of our methods and an improvement of 2.17%, 2.10% in mAP can be achieved on COCO and VOC respectively.