Journal ArticleDOI
Dynamic Selection Network for Image Inpainting
TLDR
Zhang et al. as discussed by the authors proposed a dynamic selection network (DSNet) to distinguish the corrupted regions from the valid ones throughout the entire network architecture, which may help make full use of the information in the known area.Abstract:
Image inpainting is a challenging computer vision task that aims to fill in missing regions of corrupted images with realistic contents. With the development of convolutional neural networks, many deep learning models have been proposed to solve image inpainting issues by learning information from a large amount of data. In particular, existing algorithms usually follow an encoding and decoding network architecture in which some operations with standard schemes are employed, such as static convolution, which only considers pixels with fixed grids, and the monotonous normalization style (e.g., batch normalization). However, these techniques are not well-suited for the image inpainting task because the random corrupted regions in the input images tend to mislead the inpainting process and generate unreasonable content. In this paper, we propose a novel dynamic selection network (DSNet) to solve this problem in image inpainting tasks. The principal idea of the proposed DSNet is to distinguish the corrupted region from the valid ones throughout the entire network architecture, which may help make full use of the information in the known area. Specifically, the proposed DSNet has two novel dynamic selection modules, namely, the validness migratable convolution (VMC) and regional composite normalization (RCN) modules, which share a dynamic selection mechanism that helps utilize valid pixels better. By replacing vanilla convolution with the VMC module, spatial sampling locations are dynamically selected in the convolution phase, resulting in a more flexible feature extraction process. Besides, the RCN module not only combines several normalization methods but also normalizes the feature regions selectively. Therefore, the proposed DSNet can illustrate realistic and fine-detailed images by adaptively selecting features and normalization styles. Experimental results on three public datasets show that our proposed method outperforms state-of-the-art methods both quantitatively and qualitatively.read more
Citations
More filters
Book ChapterDOI
Polysemy Deciphering Network for Robust Human–Object Interaction Detection
TL;DR: PD-Net as discussed by the authors proposes a novel polysemydeciphering network (PD-net) that decodes the visual polysemmy of verbs for HOI detection in three distinct ways: LPCA highlights important elements in human and object appearance features for each HOI category to be identified; LPFA augments human pose and spatial features using language priors, enabling verb classifiers to receive language hints that reduce intra-class variation for the same verb.
Journal ArticleDOI
Rotation-Invariant Attention Network for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a rotation-invariant attention network (RIAN) for hyperspectral image (HSI) classification, where a center spectral attention (CSpeA) module is designed to avoid the influence of other categories of pixels to suppress redundant spectral bands.
Journal ArticleDOI
Vision Transformer: An Excellent Teacher for Guiding Small Networks in Remote Sensing Image Scene Classification
TL;DR: Experimental results on the four public remote sensing data sets demonstrate that the proposed ET-GSNet method possesses the superior classification performance compared to some state-of-the-art (SOTA) methods.
Journal ArticleDOI
FFTI: Image inpainting algorithm via features fusion and two-steps inpainting
TL;DR: Zhang et al. as mentioned in this paper proposed a fine inpainting method of incomplete image based on features fusion and two-steps in-painting (FFTI), which fused the external features and internal features of the incomplete image to generate an incomplete image optimization map.
Journal ArticleDOI
A robust deformed convolutional neural network (CNN) for image denoising
TL;DR: In this paper , a robust deformed denoising CNN (RDDCNN) is proposed, which contains three blocks: a deformable block (DB), an enhanced block (EB), and a residual block (RB).
References
More filters
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.