8 accepted papers
This paper presents a control-oriented framework for automated optical tweezers (AOT), explicitly accounting for fluid dynamics and common real-world disturbances. AOT operation is strongly affected by unmeasurable flow-induced forces and stochastic perturbations under noisy vision feedback, which p
Group fairness can ensure equitable performance across different demographic subgroups for medical image analysis. However, the current fine-tuned foundation models (FMs) exhibit significant subgroup disparity. One-shot federated learning (OFL) can potentially mitigate this by leveraging cross-insti…
Digital characters are central to modern media, yet generating character videos with long-duration, consistent multi-view appearance and expressive identity remains challenging. Existing approaches either provide insufficient context to preserve identity or leverage non-character-centric information
Class-incremental learning (CIL) enables models to continuously learn new classes while addressing catastrophic forgetting. With the introduction of pre-trained models, new tuning paradigms have emerged for CIL. This paper revisits parameter-efficient fine-tuning (PEFT) methods in the context of inc…
Novel-view synthesis with sparse input views is important for real-world applications like AR/VR and autonomous driving. Recent methods have integrated depth information into NeRFs for sparse input synthesis, leveraging depth prior for geometric and spatial understanding. However, most existing work…
As a special task of natural language generation, conditional lyrics generation needs to consider the structure of generated lyrics and the relationship between lyrics and music. Due to various forms of conditions, a lyrics generation system is expected to generate lyrics conditioned on different si…
Automatic song writing (ASW) typically involves four tasks: lyric-to-lyric generation, melody-to-melody generation, lyric-to-melody generation, and melody-to-lyric generation. Previous works have mainly focused on individual tasks without considering the correlation between them, and thus a unified…
The neural network (NN) based singing voice synthesis (SVS) systems require sufficient data to train well and are are prone to over-fitting due to data scarcity. However, we often encounter data limitation problem in building SVS systems because of high data acquisition and annotation cost,. In this…