Rethinking Pseudo Labels for Semi-Supervised Object Detection

TL;DR

This paper proposes certainty-aware pseudo labels for semi-supervised object detection, improving localization and class imbalance handling, leading to state-of-the-art results on COCO and PASCAL VOC datasets.

Contribution

It introduces a novel pseudo-labeling method that estimates quality scores for classification and localization, dynamically adjusts thresholds, and reweights loss functions.

Findings

Improves SSOD performance by 1-2% AP on COCO and PASCAL VOC.

Enhances supervised baselines by up to 10% AP with limited labeled data.

Method is orthogonal and complementary to existing approaches.

Abstract

Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels as supervisory signals. However, when using pseudo labels, there is a lack of consideration in localization precision and amplified class imbalance, both of which are critical for detection tasks. In this paper, we introduce certainty-aware pseudo labels tailored for object detection, which can effectively estimate the classification and localization quality of derived pseudo labels. This is achieved by converting conventional localization as a classification task followed by refinement. Conditioned on classification and localization quality scores, we dynamically adjust the thresholds used to generate pseudo labels and reweight loss functions for each category to alleviate the class imbalance problem. Extensive experiments demonstrate that our method improves state-of-the-art SSOD performance by 1-2% AP on COCO and PASCAL VOC while being orthogonal and complementary to most existing methods. In the limited-annotation regime, our approach improves supervised baselines by up to 10% AP using only 1-10% labeled data from COCO.