Jiaying Liu

Yuhang Bai, Zichuan Huang, Wenshuo Gao et al.

Artistic text generation aims to amplify the aesthetic qualities of text while maintaining readability. It can make the text more attractive and better convey its expression, thus enjoying a wide range of application scenarios such as social media display, consumer electronics, fashion, and graphic design. Artistic text generation includes artistic text stylization and semantic typography. Artistic text stylization concentrates on the text effect overlaid upon the text, such as shadows, outlines, colors, glows, and textures. By comparison, semantic typography focuses on the deformation of the characters to strengthen their visual representation by mimicking the semantic understanding within the text. This overview paper provides an introduction to both artistic text stylization and semantic typography, including the taxonomy, the key ideas of representative methods, and the applications in static and dynamic artistic text generation. Furthermore, the dataset and evaluation metrics are introduced, and the future directions of artistic text generation are discussed. A comprehensive list of artistic text generation models studied in this review is available at https://github.com/williamyang1991/Awesome-Artistic-Typography/.

Wenhan Yang, Zixuan Hu, Lilang Lin et al.

Coding, which targets compressing and reconstructing data, and intelligence, often regarded at an abstract computational level as being centered around model learning and prediction, interweave recently to give birth to a series of significant progress. The recent trends demonstrate the potential homogeneity of these two fields, especially when deep-learning models aid these two categories for better probability modeling. For better understanding and describing from a unified perspective, inspired by the basic generally recognized principles in cognitive psychology, we formulate a novel problem of Coding for Intelligence from the category theory view. Based on the three axioms: existence of ideal coding, existence of practical coding, and compactness promoting generalization, we derive a general framework to understand existing methodologies, namely that, coding captures the intrinsic relationships of objects as much as possible, while ignoring information irrelevant to downstream tasks. This framework helps identify the challenges and essential elements in solving the specific derived Minimal Description Length (MDL) optimization problem from a broader range, providing opportunities to build a more intelligent system for handling multiple tasks/applications with coding ideas/tools. Centering on those elements, we systematically review recent processes of towards optimizing the MDL problem in more comprehensive ways from data, model, and task perspectives, and reveal their impacts on the potential CfI technical routes. After that, we also present new technique paths to fulfill CfI and provide potential solutions with preliminary experimental evidence. Last, further directions and remaining issues are discussed as well. The discussion shows our theory can reveal many phenomena and insights about large foundation models, which mutually corroborate with recent practices in feature learning.

Renqiang Luo, Tao Tang, Feng Xia et al.

Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In response, this survey delves into the existing literature on algorithmic fairness, specifically highlighting its multifaceted social consequences. We introduce a novel taxonomy based on 'tolerance', a term we define as the degree to which variations in fairness outcomes are acceptable, providing a structured approach to understanding the subtleties of fairness within algorithmic decisions. Our systematic review covers diverse industries, revealing critical insights into the balance between algorithmic decision making and social equity. By synthesizing these insights, we outline a series of emerging challenges and propose strategic directions for future research and policy making, with the goal of advancing the field towards more equitable algorithmic systems.