Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Controllable Multi-domain Semantic Artwork Synthesis

We present a novel framework for multi-domain synthesis of artwork from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis. To address this problem, we propose a dataset, which we call ArtSem, that contains 40,000 images of artwork from 4 different domains with their corresponding semantic label maps. We generate the dataset by first extracting semantic maps from landscape photography and then propose a conditional Generative Adversarial Network (GAN)-based approach to generate high-quality artwork from the semantic maps without necessitating paired training data. Furthermore, we propose an artwork synthesis model that uses domain-dependent variational encoders for high-quality multi-domain synthesis. The model is improved and complemented with a simple but effective normalization method, based on normalizing both the semantic and style jointly, which we call Spatially STyle-Adaptive Normalization (SSTAN). In contrast to previous methods that only take semantic layout as input, our model is able to learn a joint representation of both style and semantic information, which leads to better generation quality for synthesizing artistic images. Results indicate that our model learns to separate the domains in the latent space, and thus, by identifying the hyperplanes that separate the different domains, we can also perform fine-grained control of the synthesized artwork. By combining our proposed dataset and approach, we are able to generate user-controllable artwork that is of higher quality than existingComment: 15 pages, accepted by CVMJ, to appea

Painterly Image Harmonization in Dual Domains

Image harmonization aims to produce visually harmonious composite images by adjusting the foreground appearance to be compatible with the background. When the composite image has photographic foreground and painterly background, the task is called painterly image harmonization. There are only few works on this task, which are either time-consuming or weak in generating well-harmonized results. In this work, we propose a novel painterly harmonization network consisting of a dual-domain generator and a dual-domain discriminator, which harmonizes the composite image in both spatial domain and frequency domain. The dual-domain generator performs harmonization by using AdaIN modules in the spatial domain and our proposed ResFFT modules in the frequency domain. The dual-domain discriminator attempts to distinguish the inharmonious patches based on the spatial feature and frequency feature of each patch, which can enhance the ability of generator in an adversarial manner. Extensive experiments on the benchmark dataset show the effectiveness of our method. Our code and model are available at https://github.com/bcmi/PHDNet-Painterly-Image-Harmonization.Comment: Accepted by AAAI202

Sparse Recovery and Representation Learning

This dissertation focuses on sparse representation and dictionary learning, with three relative topics. First, in chapter 1, we study the problem of low-rank matrix recovery in the presence of prior information. We first study the recovery of low-rank matrices with a necessary and sufficient condition, called the Null Space Property, for exact recovery from compressively sampled measurements using nuclear norm minimization. Here, we provide an alternative theoretical analysis of the bound on the number of random Gaussian measurements needed for the condition to be satisfied with high probability. We then study low-rank matrix recovery when prior information is available. We analyze an existing algorithm, provide the necessary and sufficient conditions for exact recovery and show that the existing algorithm is limited in certain cases. We provide an alternative recovery algorithm to deal with the drawback and provide sufficient recovery conditions based on that. In chapter 2, we study the problem of learning a sparsifying dictionary of a set of data, focusing on learning dictionaries that admit fast transforms. Inspired by the Fast Fourier Transform, we propose a learning algorithm involving $O(N)$ unknown parameters for a $N\times N$ linear transformation matrix. Empirically, our algorithm can produce dictionaries that provide lower numerical sparsity for the sparse representation of images than the Discrete Fourier Transformation (DFT). Additionally, due to its structure, the learned dictionary can recover the original signal from the sparse representation in $O(N\log N)$ computations. In chapter 3, we study the representation learning problem in a more complex setting. We use the concept of dictionary learning and apply it in a deep generative model. Motivated by an application in the computer gaming industry where designers needs to have an urban layout generation tool that allows fast generation and modification, we present a novel solution to synthesize high quality building placements using conditional generative latent optimization together with adversarial training. The capability of the proposed method is demonstrated in various examples. The inference is nearly in real time, thus it can assist designers to iterate their designs of virtual cities quickly

Creative Painting with Latent Diffusion Models

Artistic painting has achieved significant progress during recent years. Using an autoencoder to connect the original images with compressed latent spaces and a cross attention enhanced U-Net as the backbone of diffusion, latent diffusion models (LDMs) have achieved stable and high fertility image generation. In this paper, we focus on enhancing the creative painting ability of current LDMs in two directions, textual condition extension and model retraining with Wikiart dataset. Through textual condition extension, users' input prompts are expanded with rich contextual knowledge for deeper understanding and explaining the prompts. Wikiart dataset contains 80K famous artworks drawn during recent 400 years by more than 1,000 famous artists in rich styles and genres. Through the retraining, we are able to ask these artists to draw novel and creative painting on modern topics. Direct comparisons with the original model show that the creativity and artistry are enriched.Comment: 17pages, 12 figure

Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview

This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture