5 research outputs found
A Lightweight Approach for Network Intrusion Detection based on Self-Knowledge Distillation
Network Intrusion Detection (NID) works as a kernel technology for the
security network environment, obtaining extensive research and application.
Despite enormous efforts by researchers, NID still faces challenges in
deploying on resource-constrained devices. To improve detection accuracy while
reducing computational costs and model storage simultaneously, we propose a
lightweight intrusion detection approach based on self-knowledge distillation,
namely LNet-SKD, which achieves the trade-off between accuracy and efficiency.
Specifically, we carefully design the DeepMax block to extract compact
representation efficiently and construct the LNet by stacking DeepMax blocks.
Furthermore, considering compensating for performance degradation caused by the
lightweight network, we adopt batch-wise self-knowledge distillation to provide
the regularization of training consistency. Experiments on benchmark datasets
demonstrate the effectiveness of our proposed LNet-SKD, which outperforms
existing state-of-the-art techniques with fewer parameters and lower
computation loads.Comment: Accepted to IEEE ICC 202
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
Exemplar-based colorization approaches rely on reference image to provide
plausible colors for target gray-scale image. The key and difficulty of
exemplar-based colorization is to establish an accurate correspondence between
these two images. Previous approaches have attempted to construct such a
correspondence but are faced with two obstacles. First, using luminance
channels for the calculation of correspondence is inaccurate. Second, the dense
correspondence they built introduces wrong matching results and increases the
computation burden. To address these two problems, we propose Semantic-Sparse
Colorization Network (SSCN) to transfer both the global image style and
detailed semantic-related colors to the gray-scale image in a coarse-to-fine
manner. Our network can perfectly balance the global and local colors while
alleviating the ambiguous matching problem. Experiments show that our method
outperforms existing methods in both quantitative and qualitative evaluation
and achieves state-of-the-art performance.Comment: Accepted by ECCV2022; 14 pages, 10 figure
Regulation of Thermal Emission Position in Biased Graphene
A very attractive advantage of graphene is that its Fermi level can be regulated by electrostatic bias doping. It is of great significance to investigate and control the spatial location of graphene emission for graphene thermal emitters, in addition to tuning the emission intensity and emission spectrum. Here, we present a detailed theoretical model to describe the graphene emission characteristics versus gate voltages. The experimentally observed movement of the emission spot and temperature distribution of graphene emitters are basically in agreement with those from the theoretical model. Our results provide a simple method to predict the behavior of graphene emitters that is beneficial for achieving the spatial dynamic regulation of graphene infrared emission arrays
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D ++, is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D ++, and our new evaluation pipeline at https://realy3dface.com.</p