Online Open-set Semi-supervised Object Detection via Semi-supervised Outlier Filtering

Open-set semi-supervised object detection (OSSOD) methods aim to utilize practical unlabeled datasets with out-of-distribution (OOD) instances for object detection. The main challenge in OSSOD is distinguishing and filtering the OOD instances from the in-distribution (ID) instances during pseudo-labeling. The previous method uses an offline OOD detection network trained only with labeled data for solving this problem. However, the scarcity of available data limits the potential for improvement. Meanwhile, training separately leads to low efficiency. To alleviate the above issues, this paper proposes a novel end-to-end online framework that improves performance and efficiency by mining more valuable instances from unlabeled data. Specifically, we first propose a semi-supervised OOD detection strategy to mine valuable ID and OOD instances in unlabeled datasets for training. Then, we constitute an online end-to-end trainable OSSOD framework by integrating the OOD detection head into the object detector, making it jointly trainable with the original detection task. Our experimental results show that our method works well on several benchmarks, including the partially labeled COCO dataset with open-set classes and the fully labeled COCO dataset with the additional large-scale open-set unlabeled dataset, OpenImages. Compared with previous OSSOD methods, our approach achieves the best performance on COCO with OpenImages by +0.94 mAP, reaching 44.07 mAP

Semi-Supervised Object Detection in the Open World

Existing approaches for semi-supervised object detection assume a fixed set of classes present in training and unlabeled datasets, i.e., in-distribution (ID) data. The performance of these techniques significantly degrades when these techniques are deployed in the open-world, due to the fact that the unlabeled and test data may contain objects that were not seen during training, i.e., out-of-distribution (OOD) data. The two key questions that we explore in this paper are: can we detect these OOD samples and if so, can we learn from them? With these considerations in mind, we propose the Open World Semi-supervised Detection framework (OWSSD) that effectively detects OOD data along with a semi-supervised learning pipeline that learns from both ID and OOD data. We introduce an ensemble based OOD detector consisting of lightweight auto-encoder networks trained only on ID data. Through extensive evalulation, we demonstrate that our method performs competitively against state-of-the-art OOD detection algorithms and also significantly boosts the semi-supervised learning performance in open-world scenarios

Semi-supervised Salient Object Detection with Effective Confidence Estimation

The success of existing salient object detection models relies on a large pixel-wise labeled training dataset, which is time-consuming and expensive to obtain. We study semi-supervised salient object detection, with access to a small number of labeled samples and a large number of unlabeled samples. Specifically, we present a pseudo label based learn-ing framework with a Conditional Energy-based Model. We model the stochastic nature of human saliency labels using the stochastic latent variable of the Conditional Energy-based Model. It further enables generation of a high-quality pixel-wise uncertainty map, highlighting the reliability of corresponding pseudo label generated for the unlabeled sample. This minimises the contribution of low-certainty pseudo labels in optimising the model, preventing the error propagation. Experimental results show that the proposed strategy can effectively explore the contribution of unlabeled data. With only 1/16 labeled samples, our model achieves competitive performance compared with state-of-the-art fully-supervised models

Semi- and Weakly-Supervised Domain Generalization for Object Detection

Object detectors do not work well when domains largely differ between training and testing data. To solve this problem, domain generalization approaches, which require training data with ground-truth labels from multiple domains, have been proposed. However, it is time-consuming and labor-intensive to collect those data for object detection because not only class labels but also bounding boxes must be annotated. To overcome the problem of domain gap in object detection without requiring expensive annotations, we propose to consider two new problem settings: semi-supervised domain generalizable object detection (SS-DGOD) and weakly-supervised DGOD (WS-DGOD). In contrast to the conventional domain generalization for object detection that requires labeled data from multiple domains, SS-DGOD and WS-DGOD require labeled data only from one domain and unlabeled or weakly-labeled data from multiple domains for training. We show that object detectors can be effectively trained on the proposed settings with the same student-teacher learning framework, where a student network is trained with pseudo labels output from a teacher on the unlabeled or weakly-labeled data. The experimental results demonstrate that the object detectors trained on the proposed settings significantly outperform baseline detectors trained on one labeled domain data and perform comparably to or better than those trained on unsupervised domain adaptation (UDA) settings, while ours do not use target domain data for training in contrast to UDA