The Biogeography of Green Algae Associated with Red Snow in Japan

第3回極域科学シンポジウム/第34回極域生物シンポジウム 11月27日（火） 国立極地研究所 ３階ラウン

Action Class Relation Detection and Classification Across Multiple Video Datasets

The Meta Video Dataset (MetaVD) provides annotated relations between action classes in major datasets for human action recognition in videos. Although these annotated relations enable dataset augmentation, it is only applicable to those covered by MetaVD. For an external dataset to enjoy the same benefit, the relations between its action classes and those in MetaVD need to be determined. To address this issue, we consider two new machine learning tasks: action class relation detection and classification. We propose a unified model to predict relations between action classes, using language and visual information associated with classes. Experimental results show that (i) pre-trained recent neural network models for texts and videos contribute to high predictive performance, (ii) the relation prediction based on action label texts is more accurate than based on videos, and (iii) a blending approach that combines predictions by both modalities can further improve the predictive performance in some cases.Comment: Accepted to Pattern Recognition Letters. 12 pages, 4 figure

Learning Decorrelated Representations Efficiently Using Fast Fourier Transform

Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features. Although these models are as effective as conventional representation learning models, their training can be computationally demanding if the dimension d of the projected embeddings is high. As the regularizers are defined in terms of individual elements of a cross-correlation or covariance matrix, computing the loss for n samples takes O(n d^2) time. In this paper, we propose a relaxed decorrelating regularizer that can be computed in O(n d log d) time by Fast Fourier Transform. We also propose an inexpensive technique to mitigate undesirable local minima that develop with the relaxation. The proposed regularizer exhibits accuracy comparable to that of existing regularizers in downstream tasks, whereas their training requires less memory and is faster for large d. The source code is available.Comment: Accepted for CVPR 202

Growth and applications of GeSn-related group-IV semiconductor materials

We review the technology of Ge1−xSnx-related group-IV semiconductor materials for developing Si-based nanoelectronics. Ge1−xSnx-related materials provide novel engineering of the crystal growth, strain structure, and energy band alignment for realising various applications not only in electronics, but also in optoelectronics. We introduce our recent achievements in the crystal growth of Ge1−xSnx-related material thin films and the studies of the electronic properties of thin films, metals/Ge1−xSnx, and insulators/Ge1−xSnx interfaces. We also review recent studies related to the crystal growth, energy band engineering, and device applications of Ge1−xSnx-related materials, as well as the reported performances of electronic devices using Ge1−xSnx related materials