Search Results for author:
Found 22 papers, 15 papers with code
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
To this end, we propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID).
Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
We apply this method to fine-tune MusicGen, a leading autoregressive music generation model.
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged.
The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models.
Content-based Controls For Music Large Language Modeling
We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track.
Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing
Creating music is iterative, requiring varied methods at each stage.
AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning
The students represent the future generation of AI and music researchers.
Exploring XAI for the Arts: Explaining Latent Space in Generative Music
We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions.
Continual Learning for Abdominal Multi-Organ and Tumor Segmentation
The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation
In this study, we explore the representation mapping from the domain of visual arts to the domain of music, with which we can use visual arts as an effective handle to control music generation.
Learning Hierarchical Metrical Structure Beyond Measures
In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores.
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model
Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mass of music archives.
Fast AdvProp
Specifically, our modifications in Fast AdvProp are guided by the hypothesis that disentangled learning with adversarial examples is the key for performance improvements, while other training recipes (e. g., paired clean and adversarial training samples, multi-step adversarial attackers) could be largely simplified.
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients.
A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset.
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images.
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).
Learning Interpretable Representation for Controllable Polyphonic Music Generation
While deep generative models have become the leading methods for algorithmic composition, it remains a challenging problem to control the generation process because the latent variables of most deep-learning models lack good interpretability.
When Radiology Report Generation Meets Knowledge Graph
In addition, we proposed a new evaluation metric for radiology image reporting with the assistance of the same composed graph.
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation
3D convolution neural networks (CNN) have been proved very successful in parsing organs or tumours in 3D medical images, but it remains sophisticated and time-consuming to choose or design proper 3D networks given different task contexts.
