Introspective Deep Metric Learning for Image Retrieval

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images. Conventional deep metric learning methods produce confident semantic distances between images regardless of the uncertainty level. However, we argue that a good similarity model should consider the semantic discrepancies with caution to better deal with ambiguous images for more robust training. To achieve this, we propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. We further propose an introspective similarity metric to make similarity judgments between images considering both their semantic differences and ambiguities. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling and attains state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets for image retrieval and clustering. We further provide an in-depth analysis of our framework to demonstrate the effectiveness and reliability of IDML. Code is available at: https://github.com/wzzheng/IDML.Comment: The extended version of this paper is accepted to T-PAMI. Source code available at https://github.com/wzzheng/IDM

OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions

The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning. However, with the availability of massive labeled data, a natural question emerges: how to train a better model with both self and full supervision signals? In this paper, we propose Omni-suPErvised Representation leArning with hierarchical supervisions (OPERA) as a solution. We provide a unified perspective of supervisions from labeled and unlabeled data and propose a unified framework of fully supervised and self-supervised learning. We extract a set of hierarchical proxy representations for each image and impose self and full supervisions on the corresponding proxy representations. Extensive experiments on both convolutional neural networks and vision transformers demonstrate the superiority of OPERA in image classification, segmentation, and object detection. Code is available at: https://github.com/wangck20/OPERA.Comment: Source code available at: https://github.com/wangck20/OPER

Transcriptional suppression of breast cancer resistance protein (BCRP) by wild-type p53 through the NF-κB pathway in MCF-7 cells

AbstractBreast cancer resistance protein (BCRP) has been shown to confer multidrug resistance, but the mechanisms of its regulation are poorly understood. Here, we investigate the effects of wild-type and mutant p53, and nuclear factor kappa-B (NF-κB) (p50) on BCRP promoter activity in MCF-7 cells. Our results demonstrated that wild-type p53 markedly suppressed BCRP activity and enhanced the chemosensitivity of cells to mitoxantrone, whereas mutant p53 had little inhibitory effect. After inhibition of NF-κB, similar results were obtained. Following knockdown of endogenous p53, BCRP and p50 expressions were increased, and the chemosensitivity of the cells to mitoxantrone was decreased. We conclude that wild-type p53 acts as a negative regulator of BCRP gene transcription