Few-shot object detection aims to detect instances of specific categories in a query image with only a handful of support samples. Although this takes less effort than obtaining enough annotated images for supervised object detection, it results in a
far inferior performance compared to the conventional object detection methods. In this paper, we propose a meta-learning-based approach that considers the unique characteristics of each support sample. Rather than simply averaging the information of the support samples to generate a single prototype per category, our method can better utilize the information of each support sample by treating each support sample as an individual prototype. Specifically, we introduce two types of attention mechanisms for aggregating the query and support feature maps. The first is to refine the information of few-shot samples by extracting shared information between the support samples through attention. Second, each support sample is used as a class code to leverage the information by comparing similarities between each support feature and query features. Our proposed method is complementary to the previous methods, making it easy to plug and play for further improvement. We have evaluated our method on PASCAL VOC and COCO benchmarks, and the results verify the effectiveness of our method. In particular, the advantages of our method are maximized when there is more diversity among support data.
Deep-tissue optical imaging suffers from the reduction of resolving power due to tissue-induced optical aberrations and multiple scattering noise. Reflection matrix approaches recording the maps of backscattered waves for all the possible orthogonal
input channels have provided formidable solutions for removing severe aberrations and recovering the ideal diffraction-limited spatial resolution without relying on fluorescence labeling and guide stars. However, measuring the full input-output response of the tissue specimen is time-consuming, making the real-time image acquisition difficult. Here, we present the use of a time-reversal matrix, instead of the reflection matrix, for fast high-resolution volumetric imaging of a mouse brain. The time-reversal matrix reduces two-way problem to one-way problem, which effectively relieves the requirement for the coverage of input channels. Using a newly developed aberration correction algorithm designed for the time-reversal matrix, we demonstrated the correction of complex aberrations using as small as 2 % of the complete basis while maintaining the image reconstruction fidelity comparable to the fully sampled reflection matrix. Due to nearly 100-fold reduction in the matrix recording time, we could achieve real-time aberration-correction imaging for a field of view of 40 x 40 microns (176 x 176 pixels) at a frame rate of 80 Hz. Furthermore, we demonstrated high-throughput volumetric adaptive optical imaging of a mouse brain by recording a volume of 128 x 128 x 125 microns (568 x 568 x 125 voxels) in 3.58 s, correcting tissue aberrations at each and every 1-micron depth section, and visualizing myelinated axons with a lateral resolution of 0.45 microns and an axial resolution of 2 microns.
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 networks 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 networks 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.
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 multipl
e 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.
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 $D$, den
oted 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}.
In this paper, we treat the image generation task using an autoencoder, a representative latent model. Unlike many studies regularizing the latent variables distribution by assuming a manually specified prior, we approach the image generation task us
ing 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.
