ABSTRACT
Vision transformers (ViTs) have become one of the dominant frameworks for vision tasks in recent years because of their ability to efficiently capture long-range dependencies in image recognition tasks using self-attention. In fact, both CNNs and ViTs have advantages and disadvantages in vision tasks, and some studies suggest that the use of both may be an effective way to balance performance and computational cost. In this paper, we propose a new hybrid network based on CNN and transformer, using CNN to extract local features and transformer to capture long-distance dependencies. We also proposed a new feature map resolution reduction based on Discrete Cosine Transform and self-attention, named DCT-Attention Down-sample (DAD). Our DctViT-L achieves 84.8% top-1 accuracy on ImageNet 1K, far outperforming CMT, Next-ViT, SpectFormer and other state-of-the-art models, with lower computational costs. Using DctViT-B as the backbone, RetinaNet can achieve 46.8% mAP on COCO val2017, which improves mAP by 2.5% and 1.1% with less calculation cost compared with CMT-S and SpectFormer as the backbone.
Subject(s)
Image Interpretation, Computer-AssistedABSTRACT
Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR; however, the existing CNN-based SR methods cannot fully search for background information in the measurement of feature extraction. In addition, in most cases, different scale factors of image SR are assumed to be different assignments and completed by training different models, which does not meet the actual application requirements. To solve these problems, we propose a multi-scale learning wavelet attention network (MLWAN) model for image SR. Specifically, the proposed model consists of three parts. In the first part, low-level features are extracted from the input image through two convolutional layers, and then a new channel-spatial attention mechanism (CSAM) block is concatenated. In the second part, CNN is used to predict the highest-level low-frequency wavelet coefficients, and the third part uses recursive neural networks (RNN) with different scales to predict the wavelet coefficients of the remaining subbands. In order to further achieve lightweight, an effective channel attention recurrent module (ECARM) is proposed to reduce network parameters. Finally, the inverse discrete wavelet transform (IDWT) is used to reconstruct HR image. Experimental results on public large-scale datasets demonstrate the superiority of the proposed model in terms of quantitative indicators and visual effects.
Subject(s)
Learning , Neural Networks, Computer , Videotape Recording , Image Processing, Computer-Assisted , RecordsABSTRACT
We describe an innovative form of polymer micro-tubes with diverse functions including biodegradation, magnetic manipulation, and photothermal effect that employs and activates photothermal therapy to target cancer cells. The micro-tube comprised soybean protein isolate, poly-l-glutamic acid, magnetite nanoparticles, plus gold nanoparticles. Through electrostatic force, these components, with opposite charges, formed pairs of layers in the pores of the template, various bilayers of soybean protein isolate and poly-l-glutamic acid served as the biodegradable building wall to each micro-tube. The layers of magnetite nanoparticle functionalized micro-tubes enabled the micro-tube manipulate to target the cancer cells by using an external magnetic field. The photo-thermal effect of the layer of gold nanoparticles on the outer surface of the micro-tubes, when under irradiation and when brought about by the near infrared radiation, elevated each sample's temperature. In addition, and when under the exposure of the near infrared radiation, the elevated temperature of the suspension of the micro-tubes, likewise with a concentration of 0.2 mg/mL, and similarly with a power of 2 W and as well maintained for 10 min, elevated the temperature of the suspension beyond 42 °C. Such temperatures induced apoptosis of target cancer cells through the effect of photothermal therapy. The findings assert that structured micro-tubes have a promising application as a photothermal agent. From this assertion, the implications are that this multifunctional agent will significantly improve the methodology for cancer diagnosis and therapy.
