RankDNN: Learning to Rank for Few-shot Learning

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankMLP can be built on top of any state-of-the-art feature extractors, and our entire deep neural network is called the ranking deep neural network, or RankDNN. Meanwhile, RankDNN can be flexibly fused with other post-processing methods. During the meta test, RankDNN ranks support images according to their similarity with the query samples, and each query sample is assigned the class label of its nearest neighbor. Experiments demonstrate that RankDNN can effectively improve the performance of its baselines based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning benchmarks, including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments on the cross-domain challenge demonstrate the superior transferability of RankDNN.The code is available at: https://github.com/guoqianyu-alberta/RankDNN.Comment: 12 pages, 4 figures. Accepted to AAAI2023. The code is available at: https://github.com/guoqianyu-alberta/RankDN

Single-Crystalline Pyramidal TiCxParticles Grown by Biphase Diffusion Synthesis

© 2022 American Chemical Society.In this study, we present single-crystalline pyramid-shaped (SP) TiCx particles synthesized on a stacked melt (copper)-solid (titanium) substrate using a biphase diffusion synthesis (BDS) method, in which different sizes ranging from nano- to micrometer scale were obtained within the copper melt with the {100} planes exposed to air. Direct observation and further plasma treatment of the pyramids at different self-assembly stages facilitated the investigation of their growth mode, especially in the horizontal plane. The dendritic growth mode along with the edge and corner-shared modes of the SP TiCx particles frozen on the copper surface was investigated. With SP TiCx particles stacked on top, MoS2-based phototransistors exhibited an up to 6-fold photocurrent increase under laser illumination at different wavelengths, which was attributed to the localized surface plasmonic resonance (LSPR) effect. The BDS method is applied for the synthesis of SP TiCx particles, with a detailed investigation of the relevant growth mode and related applications, such as decoration for high-performance photodevices.11Nsciescopu