for each cell line, we constructed four 5mC datasets, in each of which the 5mC sequences are 11, 41, 71, and 101 bp (base pairs) long, respectively. The details of the datasets are summarized in Additional file (Additional file1: Table S2, Table ...
Although FPN-like feature fusion models have achieved remarkable results in the field of computer vision, they still have some shortcomings. On the one hand, as mentioned in the paper33, in the pyramid feature fusion structure, the deep feature information is transferred to the shallow features l...
DT first intensively samples the feature points of the images in the video; then, it tracks the feature points to obtain the trajectories in the video sequence and perform feature extraction and coding based on these trajectories; finally, it performs actions by using machine learning methods such...
However, the fusion methods, based on traditional or deep learning technology, have some disadvantages such as unobvious structure or texture detail loss. In this regard, a novel generative adversarial network named MSAt-GAN is proposed in this paper. It is based on multi-scale feature transfer ...
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection Zhe Wang, Siqi Fan, Xiaoliang Huo, Tongda Xu, Yan Wang, Jingjing Liu, Yilun Chen, Ya-Qin Zhang.ICRA 2024.This repository contains the official Pytorch implementation of training & evaluation...
Learning Spatial Fusion for Single-Shot Object Detection论文解读 Learning Spatial Fusion for Single-Shot Object Detection (1)目的:不同特征尺度之间的不一致性是基于特征金字塔的单阶段检测的主要缺陷。 (2)改进点:提出了新的金字塔特征融合策略,称为自适应空间特征融合(ASFF),通过学习权重参数的方式将不同层...
(Fig.2A–D). The repeat annotations were validated by running BUSCO on the hard masked genomes of each snake to ensure non-repetitive sequences were not misclassified. This saw complete BUSCOs only drop by approximately 1.3% relative to the unmasked genomes (Additional file1: Fig. S11). ...
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3. We use the proposed MSFCB to replace the sequence of two regular convolutions in the original U-Net. The first three blocks are composed of regular convolutions, and the last two blocks are composed of dilated convolutions with different rates. In addition, we use the MSFF module in ...
This technique employs a DCNN to classify an image, whereas a discrimination model analyzes the extracted color gradient features with sequence data to identify the legitimacy of input images2. Wei et al. formulated an interactive visual model that uses self-interaction, mutual interaction, multi-...