Generalized Category Discovery with Clustering Assignment Consistency

Generalized category discovery (GCD) is a recently proposed open-world task. Given a set of images consisting of labeled and unlabeled instances, the goal of GCD is to automatically cluster the unlabeled samples using information transferred from the labeled dataset. The unlabeled dataset comprises both known and novel classes. The main challenge is that unlabeled novel class samples and unlabeled known class samples are mixed together in the unlabeled dataset. To address the GCD without knowing the class number of unlabeled dataset, we propose a co-training-based framework that encourages clustering consistency. Specifically, we first introduce weak and strong augmentation transformations to generate two sufficiently different views for the same sample. Then, based on the co-training assumption, we propose a consistency representation learning strategy, which encourages consistency between feature-prototype similarity and clustering assignment. Finally, we use the discriminative embeddings learned from the semi-supervised representation learning process to construct an original sparse network and use a community detection method to obtain the clustering results and the number of categories simultaneously. Extensive experiments show that our method achieves state-of-the-art performance on three generic benchmarks and three fine-grained visual recognition datasets. Especially in the ImageNet-100 data set, our method significantly exceeds the best baseline by 15.5\% and 7.0\% on the \texttt{Novel} and \texttt{All} classes, respectively.Comment: ICONIP 2023,This paper has been nominated for ICONIP2023 Best Paper Awar

A Survey on Deep Semi-supervised Learning

Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from model design perspectives and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods. Then we offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences. In addition to the past few years' progress, we further discuss some shortcomings of existing methods and provide some tentative heuristic solutions for solving these open problems.Comment: 24 pages, 6 figure

Hop2 Interacts with the Transcription Factor CEBPα and Suppresses Adipocyte Differentiation

CCAAT enhancer binding protein (CEBP) transcription factors (TFs) are known to promote adipocyte differentiation; however, suppressors of CEBP TFs have not been reported thus far. Here, we find that homologous chromosome pairing protein 2 (Hop2) functions as an inhibitor for the TF CEBPα. We found that Hop2 mRNA is highly and specifically expressed in adipose tissue, and that ectopic Hop2 expression suppresses reporter activity induced by CEBP as revealed by DNA transfection. Recombinant and ectopically expressed Hop2 was shown to interact with CEBPα in pull-down and coimmunoprecipitation assays, and interaction between endogenous Hop2 and CEBPα was observed in the nuclei of 3T3 preadipocytes and adipocytes by immunofluorescence and coimmunoprecipitation of nuclear extracts. In addition, Hop2 stable overexpression in 3T3 preadipocytes inhibited adipocyte differentiation and adipocyte marker gene expression. These in vitro data suggest that Hop2 inhibits adipogenesis by suppressing CEBP-mediated transactivation. Consistent with a negative role for Hop2 in adipogenesis, ablation of Hop2 (Hop2−/−) in mice led to increased body weight, adipose volume, adipocyte size, and adipogenic marker gene expression. Adipogenic differentiation of isolated adipose-derived mesenchymal stem cells showed a greater number of lipid droplet–containing colonies formed in Hop2−/− adipose-derived mesenchymal stem cell cultures than in wt controls, which is associated with the increased expression of adipogenic marker genes. Finally, chromatin immunoprecipitation revealed a higher binding activity of endogenous CEBPα to peroxisome proliferator–activated receptor γ, a master adipogenic TF, and a known CEBPα target gene. Therefore, our study identifies for the first time that Hop2 is an intrinsic suppressor of CEBPα and thus adipogenesis in adipocytes

Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices

Along with the benefits of Internet of Things (IoT) come potential privacy risks, since billions of the connected devices are granted permission to track information about their users and communicate it to other parties over the Internet. Of particular interest to the adversary is the user identity which constantly plays an important role in launching attacks. While the exposure of a certain type of physical biometrics or device identity is extensively studied, the compound effect of leakage from both sides remains unknown in multi-modal sensing environments. In this work, we explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other. It is demonstrated that our method is robust to various observation noise in the wild and an attacker can comprehensively profile victims in multi-dimension with nearly zero analysis effort. Two real-world experiments on different biometrics and device IDs show that the presented approach can compromise more than 70\% of device IDs and harvests multiple biometric clusters with ~94% purity at the same time

Physical Fitness Promotion among Adolescents: Effects of a Jump Rope-Based Physical Activity Afterschool Program

Article examines the effects of a jump rope-based physical activity afterschool program on middle school students’ physical fitness. This article belongs to the special issue Physical Activity, Sedentary Behaviour and Health Interventions in Children and Adolescents

Unsupervised Classification of Polarimetric SAR Images via Riemannian Sparse Coding

Unsupervised classification plays an important role in understanding polarimetric synthetic aperture radar (PolSAR) images. One of the typical representations of PolSAR data is in the form of Hermitian positive definite (HPD) covariance matrices. Most algorithms for unsupervised classification using this representation either use statistical distribution models or adopt polarimetric target decompositions. In this paper, we propose an unsupervised classification method by introducing a sparsity-based similarity measure on HPD matrices. Specifically, we first use a novel Riemannian sparse coding scheme for representing each HPD covariance matrix as sparse linear combinations of other HPD matrices, where the sparse reconstruction loss is defined by the Riemannian geodesic distance between HPD matrices. The coefficient vectors generated by this step reflect the neighborhood structure of HPD matrices embedded in the Euclidean space and hence can be used to define a similarity measure. We apply the scheme for PolSAR data, in which we first oversegment the images into superpixels, followed by representing each superpixel by an HPD matrix. These HPD matrices are then sparse coded, and the resulting sparse coefficient vectors are then clustered by spectral clustering using the neighborhood matrix generated by our similarity measure. The experimental results on different fully PolSAR images demonstrate the superior performance of the proposed classification approach against the state-of-the-art approachesThis work was supported in part by the National Natural Science Foundation of China under Grant 61331016 and Grant 61271401 and in part by the National Key Basic Research and Development Program of China under Contract 2013CB733404. The work of A. Cherian was supported by the Australian Research Council Centre of Excellence for Robotic Vision under Project CE140100016.