5 accepted papers
In the realm of computer vision, effectively handling multi-tasks simultaneously presents a challenge that necessitates innovative solutions. To better address multiple vision problems, we introduce SeTano, an integrated Graph Neural Network (GNN)-based framework. This framework comprises a Dynamic…
The generation of high-quality stylized handwritten text images is a challenging task in computer vision and artificial intelligence. While advanced approaches using Latent Diffusion Models (LDMs) for generating stylized handwritten text have shown effectiveness, they often struggle with maintaining…
We propose MuseControlLite, a lightweight mechanism designed to fine-tune text-to-music generation models for precise conditioning using various time-varying musical attributes and reference audio signals. The key finding is that positional embeddings, which have been seldom used by text-to-music ge…
Consider the problem of minimizing an expected logarithmic loss over either the probability simplex or the set of quantum density matrices. This problem includes tasks such as solving the Poisson inverse problem, computing the maximum-likelihood estimate for quantum state tomography, and approximati…
Faithfully loading classical data into a quantum system is a core problem in quantum machine learning and various quantum information processing tasks. In this work, we propose an efficient quantum autoencoder architecture that can construct a quantum state approximating the unknown classical distri…