Mixed Supervised Object Detection with Robust Objectness Transfer

TL;DR

This paper introduces a robust objectness transfer method for mixed supervised object detection, leveraging domain-invariant features to improve detection accuracy on new categories, outperforming existing methods.

Contribution

It proposes a novel domain-invariant objectness transfer approach that enhances mixed supervised detection by better generalizing to new categories and rejecting distractors.

Findings

Outperforms existing MSD methods on benchmark datasets

Achieves state-of-the-art results on ILSVRC2013 and PASCAL VOC

Effectively rejects distractors in weakly labeled images

Abstract

In this paper, we consider the problem of leveraging existing fully labeled categories to improve the weakly supervised detection (WSD) of new object categories, which we refer to as mixed supervised detection (MSD). Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD. In our framework, we first learn domain-invariant objectness knowledge from the existing fully labeled categories. The knowledge is modeled based on invariant features that are robust to the distribution discrepancy between the existing categories and new categories; therefore the resulting knowledge would generalize well to new categories and could assist detection models to reject distractors (e.g., object parts) in weakly labeled images of new categories. Under the guidance of learned objectness knowledge, we utilize multiple instance learning (MIL) to model the concepts of both objects and distractors and to further improve the ability of rejecting distractors in weakly labeled images. Our robust objectness transfer approach outperforms the existing MSD methods, and achieves state-of-the-art results on the challenging ILSVRC2013 detection dataset and the PASCAL VOC datasets.