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Boris Debic, Luka Medvidovic

The use of personalized artificial intelligence (AI) models in assistive healthcare presents a number of ethical and legal challenges, they are examined in this paper. We look at a number of situations in which AI models have close interactions with persons under care. In particular we are interested in the question of model and data ownership, privacy, and ethical implications. The paper also surveys the existing regulatory environment, including the US Congress's legislative initiatives and the US Federal Trade Commission's examination of AI. Additionally, it covers AI strategies such as modular AI and discusses possible solutions to the issues described. We present an overview of the current outstanding problems and with this work offer a researched and organized contribution for a public discussion of responsible application of AI in this field of healthcare. Our selection of topics is guided by keeping in mind the key stakeholders: technology providers, healthcare or care providers, care beneficiaries and their families.

Shenghui Chen, Daniel Fried, Ufuk Topcu

Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent in this game with a human as the other player. To solve this problem, we propose a communication-based approach comprising a language module and a planning module. The language module translates natural language messages into and from a finite set of flags, a compact representation defined to capture player intents. The planning module leverages these flags to compute a policy using an asymmetric information-set Monte Carlo tree search with flag exchange algorithm we present. We evaluate the effectiveness of this approach in a testbed based on Gnomes at Night, a search-and-find maze board game. Results of human subject experiments show that communication narrows the information gap between players and enhances human-agent cooperation efficiency with fewer turns.

Vaibhav Balloli, Sara Beery, Elizabeth Bondi-Kelly

Image retrieval plays a pivotal role in applications from wildlife conservation to healthcare, for finding individual animals or relevant images to aid diagnosis. Although deep learning techniques for image retrieval have advanced significantly, their imperfect real-world performance often necessitates including human expertise. Human-in-the-loop approaches typically rely on humans completing the task independently and then combining their opinions with an AI model in various ways, as these models offer very little interpretability or correctability. To allow humans to intervene in the AI model instead, thereby saving human time and effort, we adapt the Concept Bottleneck Model (CBM) and propose CHAIR. CHAIR (a) enables humans to correct intermediate concepts, which helps \textit{improve} embeddings generated, and (b) allows for flexible levels of intervention that accommodate varying levels of human expertise for better retrieval. To show the efficacy of \methodname, we demonstrate that our method performs better than similar models on image retrieval metrics without any external intervention. Furthermore, we also showcase how human intervention helps further improve retrieval performance, thereby achieving human-AI complementarity.

Zhanjie Zhang, Quanwei Zhang, Huaizhong Lin, Wei Xing, Juncheng Mo, Shuaicheng Huang, Jinheng Xie, Guangyuan Li, Junsheng Luan, Lei Zhao 等

Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always introduce obvious artifacts and disharmonious patterns. Recently, large-scale pre-trained diffusion models opened up a new way for generating highly realistic artistic stylized images. However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. Specifically, we introduce a Step-aware and Layer-aware Prompt Space, a set of learnable prompts, which can learn the style information from the collection of artworks and dynamically adjusts the input images' content structure and style pattern. To train our prompt space, we propose a novel inversion method, called Step-ware and Layer-aware Prompt Inversion, which allows the prompt space to learn the style information of the artworks collection. In addition, we inject a pre-trained conditional branch of ControlNet into our LSAST, which further improved our framework's ability to maintain content structure. Extensive experiments demonstrate that our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods. Code is available at https://github.com/Jamie-Cheung/LSAST.

Yixiao Zhang, Yukara Ikemiya, Gus Xia, Naoki Murata, Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Yuki Mitsufuji, Simon Dixon

Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, the task of editing these generated music remains a significant challenge. This paper introduces a novel approach to edit music generated by such models, enabling the modification of specific attributes, such as genre, mood, and instrument, while maintaining other aspects unchanged. Our method transforms text editing to the latent space manipulation, and adds an additional constraint to enforce consistency. It seamlessly integrates with existing pretrained text-to-music diffusion models without requiring additional training. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations. We also show the practical applicability of our approach in real-world music editing scenarios.

Liguo Zhang, Yalong Zhu, Achref Benarab, Yusen Ma, Yuxin Dong, Jianguo Sun

As a typical visual art form, Chinese calligraphy has a long history and aesthetic value. However, current methods for generating Chinese fonts still struggle with complex character shapes and lack personalized writing styles. To address these issues, we propose a font generation method for Chinese Calligraphy based on diffusion model incorporating physical information neural network (PINN), which is named DP-Font. Firstly, the multi-attribute guidance is combined to guide the generation process of the diffusion model and introduce the critical constraint of stroke order in Chinese characters, aiming to significantly improve the quality of the generated results. We then incorporate physical constraints into the neural network loss function, utilizing physical equations to provide in-depth guidance and constraints on the learning process. By learning the movement rule of the nib and the diffusion pattern of the ink, DP-Font can generate personalized calligraphy styles. The generated fonts are very close to the calligraphers' works. Compared with existing deep learning-based techniques, DP-Font has made significant progress in enhancing the physical plausibility of the model, generating more realistic and high-quality results.

Wei Zeng, Xian He, Ye Wang

Piano audio-to-score transcription (A2S) is an important yet underexplored task with extensive applications for music composition, practice, and analysis. However, existing end-to-end piano A2S systems faced difficulties in retrieving bar-level information such as key and time signatures, and have been trained and evaluated with only synthetic data. To address these limitations, we propose a sequence-to-sequence (Seq2Seq) model with a hierarchical decoder that aligns with the hierarchical structure of musical scores, enabling the transcription of score information at both the bar and note levels by multi-task learning. To bridge the gap between synthetic data and recordings of human performance, we propose a two-stage training scheme, which involves pre-training the model using an expressive performance rendering (EPR) system on synthetic audio, followed by fine-tuning the model using recordings of human performance. To preserve the voicing structure for score reconstruction, we propose a pre-processing method for **Kern scores in scenarios with an unconstrained number of voices. Experimental results support the effectiveness of our proposed approaches, in terms of both transcription performance on synthetic audio data in comparison to the current state-of-the-art, and the first experiment on human recordings.

Yuqiu Xie, Bolin Jiang, Jiawei Li, Naiqi Li, Bin Chen, Tao Dai, Yuang Peng, Shu-Tao Xia

Traditional QR codes consist of a grid of black-and-white square modules, which lack aesthetic appeal and meaning for human perception. This has motivated recent research to beautify the visual appearance of QR codes. However, there exists a trade-off between the visual quality and scanning-robustness of the image, causing outputs of previous works are simple and of low quality to ensure scanning-robustness. In this paper, we introduce a novel approach GladCoder to generate stylized QR codes that are personalized, natural, and text-driven. Its pipeline includes a Depth-guided Aesthetic QR code Generator (DAG) to improve quality of image foreground, and a GrayscaLe-Aware Denoising (GLAD) process to enhance scanning-robustness. The overall pipeline is based on diffusion models, which allow users to create stylized QR images from a textual prompt to describe the image and a textual input to be encoded. Experiments demonstrate that our method can generate stylized QR code with appealing perception details, while maintaining robust scanning reliability under real world applications.

Zihao Wang, Shuyu Li, Tao Zhang, Qi Wang, Pengfei Yu, Jinyang Luo, Yan Liu, Ming Xi, Kejun Zhang

The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due to semantic gaps between Music Information Retrieval (MIR) algorithms and human understanding, discrepancies between professionals and the public, and low precision of annotations, existing music description datasets cannot serve as benchmarks. To this end, we present MuChin, the first open-source music description benchmark in Chinese colloquial language, designed to evaluate the performance of multimodal LLMs in understanding and describing music. We established the Caichong Music Annotation Platform (CaiMAP) that employs an innovative multi-person, multi-stage assurance method, and recruited both amateurs and professionals to ensure the precision of annotations and alignment with popular semantics. Utilizing this method, we built a large-scale, private dataset with multi-dimensional, high-precision music annotations, the Caichong Music Dataset (CaiMD), and carefully selected 1,000 high-quality entries to serve as the test set for MuChin. Based on MuChin, we analyzed the discrepancies between professionals and amateurs in terms of music description, and empirically demonstrated the effectiveness of CaiMD for fine-tuning LLMs. Ultimately, we employed MuChin to evaluate existing music understanding models on their ability to provide colloquial descriptions of music.

Nikita Srivatsan, Ke Chen, Shlomo Dubnov, Taylor Berg-Kirkpatrick

In this paper we put forward a new approach to music captioning, the task of automatically generating natural language descriptions for songs. These descriptions are useful both for categorization and analysis, and also from an accessibility standpoint as they form an important component of closed captions for video content. Our method supplements an audio encoding with a retriever, allowing the decoder to condition on multimodal signal both from the audio of the song itself as well as a candidate caption identified by a nearest neighbor system. This lets us retain the advantages of a retrieval based approach while also allowing for the flexibility of a generative one. We evaluate this system on a dataset of 200k music-caption pairs scraped from Audiostock, a royalty-free music platform, and on MusicCaps, a dataset of 5.5k pairs. We demonstrate significant improvements over prior systems across both automatic metrics and human evaluation.

Damien Sprockeels, Peter Van Roy

The realm of music composition with artificial intelligence stands as a pertinent and evolving field, attracting increasing interest and exploration in contemporary research and practice. This paper presents a constraint-programming based approach to generating four-voice diatonic chord progressions according to established rules of tonal harmony. It uses the strength of constraint programming as a formal logic to rigorously model musical rules and to offer complete control over the set of rules that are enforced. This allows composers to iteratively interact with the model, adding and removing constraints, allowing them to shape the solutions according to their preferences. We define a constraint model of basic tonal harmony, called Diatony. We show that our implementation using the Gecode solver finds optimal solutions in reasonable time and we show how it can be used by a composer to aid in their composition process.

Youngrok Song, Gunhee Cho, HyunJu Kim, Youngjune Kim, Byung-Chull Bae, Yun-Gyung Cheong

Conflict is a critical element in the narrative, inciting dramatic tension. This paper introduces CNGCI (Conflict-driven Narrative Generation through Commonsense Inference), a neuro-symbolic framework designed to generate coherent stories embedded with conflict using commonsense inference. Our framework defines narrative conflict by leveraging the concept of a soft causal threat, where conflict serves as an obstacle that reduces the likelihood of achieving the protagonist's goal by weakening the causal link between context and goal through defeasible inference. Comparative studies against multiple story generation baselines utilizing commonsense reasoning show that our framework outperforms the baselines in creating narratives that distinctly embody conflict while maintaining coherency.

Geonho Son, Juhun Lee, Simon S. Woo

The fabrication of visual misinformation on the web and social media has increased exponentially with the advent of foundational text-to-image diffusion models. Namely, Stable Diffusion inpainters allow the synthesis of maliciously inpainted images of personal and private figures, and copyrighted contents, also known as deepfakes. To combat such generations, a disruption framework, namely Photoguard, has been proposed, where it adds adversarial noise to the context image to disrupt their inpainting synthesis. While their framework suggested a diffusion-friendly approach, the disruption is not sufficiently strong and it requires a significant amount of GPU and time to immunize the context image. In our work, we re-examine both the minimal and favorable conditions for a successful inpainting disruption, proposing DDD, a “Digression guided Diffusion Disruption” framework. First, we identify the most adversarially vulnerable diffusion timestep range with respect to the hidden space. Within this scope of noised manifold, we pose the problem as a semantic digression optimization. We maximize the distance between the inpainting instance's hidden states and a semantic-aware hidden state centroid, calibrated both by Monte Carlo sampling of hidden states and a discretely projected optimization in the token space. Effectively, our approach achieves stronger disruption and a higher success rate than Photoguard while lowering the GPU memory requirement, and speeding the optimization up to three times faster.

Reed Perkins, Dan Ventura

We outline a solution to the challenge of musical phrase segmentation that uses grammatical induction algorithms, a class of algorithms which infer a context-free grammar from an input sequence. We analyze the performance of five grammatical induction algorithms on three datasets using various musical viewpoint combinations. Our experiments show that the LONGESTFIRST algorithm achieves the best F1 scores across all three datasets and that input encodings that include the duration viewpoint result in the best performance.

Penousal Machado, Tiago Martins, João Correia, Luís Espírito Santo, Nuno Lourenço, João Cunha, Sérgio Rebelo, Pedro Martins, João Bicker

This paper describes our response to a unique challenge presented by the Portuguese National Press-Mint: to use Artificial Intelligence to design a commemorative coin that celebrates the "digital world". We explain the process of this coin's co-creation, from conceptualisation to production, highlighting the design process, the underlying rationale, key obstacles encountered, and the technical innovations and developments made to meet the challenge. These include the development of an evolutionary art system guided by Contrastive Language–Image Pre-training (CLIP) and Machine Learning-based aesthetic models, a system for prompt evolution, and a representation for encoding genotypes in mintable format. This collaboration produced a limited edition 10 euro silver proof coin, with a total of 4000 units minted by the National Press-Mint. The coin was met with enthusiasm, selling out within two months. This work contributes to Computational Creativity, particularly co-creativity, co-design, and digital art, and represents a significant step in using Artificial Intelligence for Numismatics.

Ang Lv, Xu Tan, Tao Qin, Tie-Yan Liu, Rui Yan

Current lyric-to-melody generation methods struggle with the lack of paired lyric-melody data to train, and the lack of adherence to composition guidelines, resulting in melodies that do not sound human-composed. To address these issues, we propose a novel paradigm called Re-creation of Creations (ROC) that combines the strengths of both rule-based and neural-based methods. ROC consists of a two-stage generation-retrieval pipeline: the creation and re-creation stages. In the creation stage, we train a melody language model using melody data to generate high-quality music fragments, which are stored in a database indexed by key features. In the re-creation stage, users provide lyrics and a preferred chord progression, and ROC infers melody features for each lyric sentence. By querying the database, we obtain relevant melody fragments that satisfy composition guidelines, and these candidates are filtered, re-ranked, and concatenated based on the guidelines and the melody language model scores. ROC offers two main advantages: it does not require paired lyric-melody data, and it incorporates commonly used composition guidelines, resulting in music that sounds more human-composed with better controllability. Both objective and subjective evaluation results on English and Chinese lyrics show the effectiveness of ROC.

Haotian Liu, Dixin Luo, Hongteng Xu

As an important mathematical concept, fractals commonly appear in nature and inspire the design of many artistic works. Although we can generate various fractal images easily based on different iterated function systems (IFSs), inferring an IFS from a given fractal image is still a challenging inverse problem for both scientific research and artistic design. In this study, we explore the potential of deep learning techniques for this problem, learning a multi-head auto-encoding model to infer typical IFSs (including Julia set and L-system) from fractal images. In principle, the proposed model encodes fractal images in a latent space and decodes their corresponding IFSs based on the latent representations. For the fractal images generated by heterogeneous IFSs, we let them share the same encoder and apply two decoders to infer the sequential and non-sequential parameters of their IFSs, respectively. By introducing one more decoder to reconstruct fractal images, we can leverage large-scale unlabeled fractal images to learn the model in a semi-supervised way, which suppresses the risk of over-fitting. Comprehensive experiments demonstrate that our method provides a promising solution to infer IFSs approximately from fractal images. Code and supplementary file are available at \url{https://github.com/HaotianLiu123/Inferring-IFSs-From-Fractal-Images}.

Liwei Lin, Gus Xia, Yixiao Zhang, Junyan Jiang

Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tasks. To bridge this gap, To address this gap, we propose a novel approach leveraging a parameter-efficient heterogeneous adapter combined with a masking training scheme. This approach enables autoregressive language models to seamlessly address music inpainting tasks. Additionally, our method integrates frame-level content-based controls, facilitating track-conditioned music refinement and score-conditioned music arrangement. We apply this method to fine-tune MusicGen, a leading autoregressive music generation model. Our experiments demonstrate promising results across multiple music editing tasks, offering more flexible controls for future AI-driven music editing tools. The source codes and a demo page showcasing our work are available at https://kikyo-16.github.io/AIR.

Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer

We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensions of music, pitch and rhythm, and considers both relative and absolute representations of these components. We evaluate our approach on four different musical understanding problems: monophonic voice separation, harmonic analysis, cadence detection, and composer identification which, in abstract terms, translate to different graph learning problems, namely, node classification, link prediction, and graph classification. Our experiments demonstrate that MusGConv improves the performance on three of the aforementioned tasks while being conceptually very simple and efficient. We interpret this as evidence that it is beneficial to include perception-informed processing of fundamental musical concepts when developing graph network applications on musical score data. All code and models are released on https://github.com/manoskary/musgconv.

Xin Jin, Qianqian Qiao, Yi Lu, Huaye Wang, Shan Gao, Heng Huang, Guangdong Li

Image aesthetic evaluation is a highly prominent research domain in the field of computer vision. In recent years, there has been a proliferation of datasets and corresponding evaluation methodologies for assessing the aesthetic quality of photographic works, leading to the establishment of a relatively mature research environment. However, in contrast to the extensive research in photographic aesthetics, the field of aesthetic evaluation for paintings and drawings has seen limited attention until the introduction of the BAID dataset in March 2023. This dataset solely comprises overall scores for high-quality artistic images. Our research marks the pioneering introduction of a multi-attribute, multi-category dataset specifically tailored to the field of painting: Aesthetics of Paintings and Drawings Dataset (APDD). The construction of APDD received active participation from 28 professional artists worldwide, along with dozens of students specializing in the field of art. This dataset encompasses 24 distinct artistic categories and 10 different aesthetic attributes. Each image in APDD has been evaluated by six professionally trained experts in the field of art, including assessments for both total aesthetic scores and aesthetic attribute scores. The final APDD dataset comprises a total of 4985 images, with an annotation count exceeding 31100 entries. Concurrently, we propose an innovative approach: Art Assessment Network for Specific Painting Styles (AANSPS), designed for the assessment of aesthetic attributes in mixed-attribute art datasets. Through this research, our goal is to catalyze advancements in the field of aesthetic evaluation for paintings and drawings, while enriching the available resources and methodologies for its further development and application. Dataset is available at https://github.com/BestiVictory/APDD.git