This paper proposes a method for embedding diffusion potentials into a hyperbolic space in order to visualize the differentiation structure consisting of diffusion and branching inherent in high-dimensional data. In recent years, the rapid development of single-cell sequencing in the field of biological information processing has made it possible to observe the evolution of gene expression levels in a snapshot-like manner as cells grow from birth to each organ or tissue. Visualization of such high-dimensional (gene pattern dimension) data is expected to provide important insights into the mechanisms of cell differentiation. Therefore, in the visualization of such data, there is a need for a system that emphasizes the "diffusion" structure that gradually shifts with time and the "branching" structure that broadly branches off into individual organs and tissues. Conventionally, the diffusion map and its extension PHATE have been developed as visualization methods specializing in diffusion structures, and hyperbolic embedding has been used as a method specializing in branching structures. However, methods that attempt to explicitly capture diffusion and branching structures simultaneously have not yet received much attention. In this paper, we focus on diffusion mapping (and its extension, PHATE), which specializes in diffusion structures, and hyperbolic embedding, which specializes in branching structures, and propose a visualization method that combines the advantages of both in order to better capture differentiaion structures consisting of diffusion and branching. As a symbolic example, we demonstrate our method using gene-cell expression data in the context of single cell analysis.

This paper proposes a Bayesian nonparametric diffusion model with a black-box warping function represented by a Gaussian process to infer potential diffusion structures latent in observed data, such as differentiation mechanisms of living cells and phylogenetic evolution processes of media information. In general, the task of inferring latent differentiation structures is very difficult to handle due to two interrelated settings. One is that the conversion mechanism between hidden structure and often higher dimensional observations is unknown (and is a complex mechanism). The other is that the topology of the hidden diffuse structure itself is unknown. Therefore, in this paper, we propose a BNP-based strategy as a natural way to deal with these two challenging settings simultaneously. Specifically, as an extension of the Gaussian process latent variable model, we propose a model in which the black box transformation from latent variable space to observed data space is represented by a Gaussian process, and introduce a BNP diffusion model for the latent variable space. We show its application to the visualization of the diffusion structure of media information and to the task of inferring cell differentiation structure from single-cell gene expression levels.

Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently illposed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abundant 3D labels, typically obtained through expensive and laborintensive annotation on LiDAR point clouds. To tackle this problem, we propose a novel weakly supervised 3D object detection framework named VSRD (Volumetric Silhouette Rendering for Detection) to train 3D object detectors without any 3D supervision but only weak 2D supervision. VSRD consists of multi-view 3D auto-labeling and subsequent training of monocular 3D object detectors using the pseudo labels generated in the auto-labeling stage. In the auto-labeling stage, we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instanceaware volumetric silhouette rendering. To directly optimize the 3D bounding boxes through rendering, we decompose the SDF of each instance into the SDF of a cuboid and the residual distance field (RDF) that represents the residual from the cuboid. This mechanism enables us to optimize the 3D bounding boxes in an end-to-end manner by comparing the rendered instance masks with the ground truth instance masks. The optimized 3D bounding boxes serve as effective training data for 3D object detection. We conduct extensive experiments on the KITTI-360 dataset, demonstrating that our method outperforms the existing weakly supervised 3D object detection methods. The code is available at https://github.com/skmhrk1209/VSRD.

Recently proposed DNN-based stereo matching methods that learn priors directly from data are known to suffer a drastic drop in accuracy in new environments. Although supervised approaches with ground truth disparity maps often work well, collecting them in each deployment environment is cumbersome and costly. For this reason, many unsupervised domain adaptation methods based on image-to-image translation have been proposed, but these methods do not preserve the geometric structure of a stereo image pair because the image-to-image translation is applied to each view separately. To address this problem, in this paper, we propose an attention mechanism that aggregates features in the left and right views, called Stereoscopic Cross Attention (SCA). Incorporating SCA to an image-to-image translation network makes it possible to preserve the geometric structure of a stereo image pair in the process of the image-to-image translation. We empirically demonstrate the effectiveness of the proposed unsupervised domain adaptation based on the image-to-image translation with SCA.