55 research outputs found
EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting
The portrait matting task aims to extract an alpha matte with complete
semantics and finely-detailed contours. In comparison to CNN-based approaches,
transformers with self-attention allow a larger receptive field, enabling it to
better capture long-range dependencies and low-frequency semantic information
of a portrait. However, the recent research shows that self-attention mechanism
struggle with modeling high-frequency information and capturing fine contour
details, which can lead to bias while predicting the portrait's contours. To
address the problem, we propose EFormer to enhance the model's attention
towards semantic and contour features. Especially the latter, which is
surrounded by a large amount of high-frequency details. We build a semantic and
contour detector (SCD) to accurately capture the distribution of semantic and
contour features. And we further design contour-edge extraction branch and
semantic extraction branch for refining contour features and complete semantic
information. Finally, we fuse the two kinds of features and leverage the
segmentation head to generate the predicted portrait matte. Remarkably, EFormer
is an end-to-end trimap-free method and boasts a simple structure. Experiments
conducted on VideoMatte240K-JPEGSD and AIM datasets demonstrate that EFormer
outperforms previous portrait matte methods.Comment: 17 pages, 6 figure
Detection-driven exposure-correction network for nighttime drone-view object detection.
Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset
Powdery Mildews Are Characterized by Contracted Carbohydrate Metabolism and Diverse Effectors to Adapt to Obligate Biotrophic Lifestyle
Powdery mildew is a widespread plant disease caused by obligate biotrophic fungal pathogens involving species-specific interactions between host and parasite. To gain genomic insights into the underlying obligate biotrophic mechanisms, we analyzed 15 microbial genomes covering powdery and downy mildews and rusts. We observed a genome-wide, massive contraction of multiple gene families in powdery mildews, such as enzymes in the carbohydrate metabolism pathway, when compared with ascomycete phytopathogens, while the fatty acid metabolism pathway maintained its integrity. We also observed significant differences in candidate secreted effector protein (CSEP) families between monocot and dicot powdery mildews, perhaps due to different selection forces. While CSEPs in monocot mildews are likely subject to positive selection causing rapid expansion, CSEP families in dicot mildews are shrinking under strong purifying selection. Our results not only illustrate obligate biotrophic mechanisms of powdery mildews driven by gene family evolution in nutrient metabolism, but also demonstrate how the divergence of CSEPs between monocot and dicot lineages might contribute to species-specific adaption
Corrigendum: Powdery Mildews Are Characterized by Contracted Carbohydrate Metabolism and Diverse Effectors to Adapt to Obligate Biotrophic Lifestyle
Neuron Learning Machine for Representation Learning
This paper presents a novel neuron learning machine (NLM) which can extract hierarchical features from data. We focus on the single-layer neural network architecture and propose to model the network based on the Hebbian learning rule. Hebbian learning rule describes how synaptic weight changes with the activations of presynaptic and postsynaptic neurons. We model the learning rule as the objective function by considering the simplicity of the network and stability of solutions. We make a hypothesis and introduce a correlation based constraint according to the hypothesis. We find that this biologically inspired model has the ability of learning useful features from the perspectives of retaining abstract information. NLM can also be stacked to learn hierarchical features and reformulated into convolutional version to extract features from 2-dimensional data
Computer vision: CCF Chinese conference, CCCV 2015, Xi'an, China, September 18-20, 2015, proceedings, part I
EI54
Osiris: A Malware Behavior Capturing System Implemented at Virtual Machine Monitor Layer
To perform behavior based malware analysis, behavior capturing is an important prerequisite. In this paper, we present Osiris system which is a tool to capture behaviors of executable files in Windows system. It collects API calls invoked not only by main process of the analysis file, but also API calls invoked by child processes which are created by main process, injected processes if process injection happens, and service processes if the main process creates services. By modifying the source code of Qemu, Osiris is implemented at the virtual machine monitor layer and has the following advantages. First, it does not rewrite the binary code of analysis file or interfere with its normal execution, so that behavior data are obtained more stealthily and transparently. Second, it employs a multi-virtual machine framework to simulate the network environment for malware analysis, so that network behaviors of a malware are stimulated to a large extend. Third, besides network environment, it also simulates most common host events to stimulate potential malicious behaviors of a malware. The experimental results show that Osiris automates the malware analysis process and provides good behavior data for the following detection algorithm
Improving small-scale dataset classification performance through weak-label samples generated by InfoGAN
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