In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark.

Rethinking Atrous Convolution for Semantic Image Segmentation

Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.

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Image Style Transfer Using Convolutional Neural Networks

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom-up, top-down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State-of-the-art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.

/pdf/stacked-hourglass-networks-for-human-pose-estimation-dlf71xnitp.pdf

Stacked Hourglass Networks for Human Pose Estimation

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce beta-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter beta that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that beta-VAE with appropriately tuned beta > 1 qualitatively outperforms VAE (beta = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, beta-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter, which can be directly optimised through a hyper parameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework

The present disclosure includes methods and systems for generating modified digital images utilizing a neural network that includes a rendering layer. In particular, the disclosed systems and methods can train a neural network to decompose an input digital image into intrinsic physical properties (e.g., such as material, illumination, and shape). Moreover, the systems and methods can substitute one of the intrinsic physical properties for a target property (e.g., a modified material, illumination, or shape). The systems and methods can utilize a rendering layer trained to synthesize a digital image to generate a modified digital image based on the target property and the remaining (unsubstituted) intrinsic physical properties. Systems and methods can increase the accuracy of modified digital images by generating modified digital images that realistically reflect a confluence of intrinsic physical properties of an input digital image and target (i.e., modified) properties.

Editing digital images utilizing a neural network with an in-network rendering layer

Disclosed are various embodiments labeling objects using multi-scale partitioning, rare class expansion, and/or spatial context techniques. An input image may be partitioned using different scale values to produce a different set of superpixels for each of the different scale values. Potential object labels for superpixels in each different set of superpixels of the input image may be assessed by comparing descriptors of the superpixels in each different set of superpixels of the input image with descriptors of reference superpixels in labeled reference images. An object label may then be assigned for a pixel of the input image based at least in part on the assessing of the potential object labels.

Labeling objects in image scenes

We present a hybrid parametric and nonparametric algorithm, exemplar cut, for generating class-specific object segmentation hypotheses. For the parametric part, we train a pylon model on a hierarchical region tree as the energy function for segmentation. For the nonparametric part, we match the input image with each exemplar by using regions to obtain a score which augments the energy function from the pylon model. Our method thus generates a set of highly plausible segmentation hypotheses by solving a series of exemplar augmented graph cuts. Experimental results on the Graz and PASCAL datasets show that the proposed algorithm achieves favorable segmentation performance against the state-of-the-art methods in terms of visual quality and accuracy.

http://ieeexplore.ieee.org/iel7/6750807/6751100/06751216.pdf

Exemplar Cut

We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focuses on a novel scenario to handle skeleton-free characters with diverse shapes, topologies, and mesh connectivities. The key idea of our method is to represent the characters in a unified articulation model so that the pose can be transferred through the correspondent parts. To achieve this, we propose a novel pose transfer network that predicts the character skinning weights and deformation transformations jointly to articulate the target character to match the desired pose. Our method is trained in a semi-supervised manner absorbing all existing character data with paired/unpaired poses and stylized shapes. It generalizes well to unseen stylized characters and inanimate objects. We conduct extensive experiments and demonstrate the effectiveness of our method on this novel task.

Skeleton-free Pose Transfer for Stylized 3D Characters

Digital image completion using deep learning is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a framework that combines generative and discriminative neural networks based on learning architecture of the generative adversarial networks. From the holey digital image, the generative neural network generates a filled digital image having hole-filling content in place of holes. The discriminative neural networks detect whether the filled digital image and the hole-filling digital content correspond to or include computer-generated content or are photo-realistic. The generating and detecting are iteratively continued until the discriminative neural networks fail to detect computer-generated content for the filled digital image and hole-filling content or until detection surpasses a threshold difficulty. Responsive to this, the image completer outputs the filled digital image with hole-filling content in place of the holey digital image's holes.

Jimei Yang

Papers

Editing digital images utilizing a neural network with an in-network rendering layer

Labeling objects in image scenes

Exemplar Cut

Skeleton-free Pose Transfer for Stylized 3D Characters

Digital image completion using deep learning