arXiv subjects

Hojun Lee

Publications and source records attributed to Hojun Lee.

22 records · Page 2Linked to original sources

arXiv2021

Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image

In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous methods train a multi-object detection network through a procedure that directly assigns the ground truth bounding boxes to the specific locations of the network's output. However, this procedure makes the training of a multi-object detection network too heuristic and complicated. In this paper, we reformulate the multi-object detection task as a problem of density estimation of bounding boxes. Instead of assigning each ground truth to specific locations of network's output, we train a network by estimating the probability density of bounding boxes in an input image using a mixture model. For this purpose, we propose a novel network for object detection called Mixture Density Object Detector (MDOD), and the corresponding objective function for the density-estimation-based training. We applied MDOD to MS COCO dataset. Our proposed method not only deals with multi-object detection problems in a new approach, but also improves detection performances through MDOD. The code is available: https://github.com/yoojy31/MDOD.

cs.CV↗

arXiv2021

Unpriortized Autoencoder For Image Generation

In this paper, we treat the image generation task using an autoencoder, a representative latent model. Unlike many studies regularizing the latent variable's distribution by assuming a manually specified prior, we approach the image generation task using an autoencoder by directly estimating the latent distribution. To this end, we introduce 'latent density estimator' which captures latent distribution explicitly and propose its structure. Through experiments, we show that our generative model generates images with the improved visual quality compared to previous autoencoder-based generative models.

cs.LG↗

arXiv2019

Laser scanning reflection-matrix microscopy for label-free in vivo imaging of a mouse brain through an intact skull

We present a laser scanning reflection-matrix microscopy combining the scanning of laser focus and the wide-field mapping of the electric field of the backscattered waves for eliminating higher-order aberrations even in the presence of strong multiple light scattering noise. Unlike conventional confocal laser scanning microscopy, we record the amplitude and phase maps of reflected waves from the sample not only at the confocal pinhole, but also at other non-confocal points. These additional measurements lead us to constructing a time-resolved reflection matrix, with which the sample-induced aberrations for the illumination and detection pathways are separately identified and corrected. We realized in vivo reflectance imaging of myelinated axons through an intact skull of a living mouse with the spatial resolution close to the ideal diffraction limit. Furthermore, we demonstrated near-diffraction-limited multiphoton imaging through an intact skull by physically correcting the aberrations identified from the reflection matrix. The proposed method is expected to extend the range of applications, where the knowledge of the detailed microscopic information deep within biological tissues is critical.

physics.bio-ph↗

arXiv2019

The phylogeny number in the aspect of triangles and diamonds of a graph

Given an acyclic digraph $D$, the competition graph of $D$, denoted by $C(D)$, is the simple graph having vertex set $V(D)$ and edge set $\{uv \mid (u, w), (v, w) \in A(D) \text{ for some } w \in V(D) \}$. The phylogeny graph of an acyclic digraph $D$, denoted by $P(D)$, is the graph with the vertex set $V(D)$ and the edge set $E(U(D)) \cup E(C(D))$ where $U(D)$ denotes the underlying graph of $D$. The notion of phylogeny graphs was introduced by Roberts and Sheng~\cite{roberts1997phylogeny} as a variant of competition graph. Moral graphs having arisen from studying Bayesian networks are the same as phylogeny graphs. In this paper, we integrate the existing theorems computing phylogeny numbers of connected graph with a small number of triangles into one proposition: for a graph $G$ containing at most two triangle, $ |E(G)|-|V(G)|-2t(G)+d(G)+1 \le p(G) \le |E(G)|-|V(G)|-t(G)+1 $ where $t(G)$ and $d(G)$ denote the number of triangles and the number of diamond in $G$, respectively. Then we show that these inequalities hold for graphs with many triangles. In the process of showing it, we derive a useful theorem which plays a key role in deducing various meaningful results including a theorem that answers a question given by Wu~{\it et al.}~\cite{Wu2019}.

math.CO↗