🏢 Southeast University

Unlocking Partial-Label Learning: A new study reveals surprisingly simple design principles for highly accurate algorithms, dramatically simplifying future research and boosting performance.

SalientLoRA unveils optimal LoRA ranks by analyzing rank salience via time-series analysis, improving fine-tuning efficiency and performance significantly.

SimVG: A simpler, faster visual grounding framework with decoupled multi-modal fusion, achieving state-of-the-art performance.

Prune and Repaint: A new content-aware method for superior image retargeting across any aspect ratio, preserving key features and avoiding artifacts.

LIVE, a novel learnable in-context vector, significantly improves visual question answering by reducing computational costs and enhancing accuracy compared to traditional ICL methods.

Lever-LM configures effective in-context demonstrations for large vision-language models using a small language model, significantly improving their performance on visual question answering and image …

Researchers derived tighter generalization bounds for label-specific representation learning (LSRL) methods, improving understanding of LSRL’s success and offering guidance for future algorithm develo…

ControlSynth Neural ODEs (CSODEs) guarantee convergence in complex dynamical systems via tractable linear inequalities, improving neural ODE modeling.

This paper presents a novel method to align vision models with human aesthetics in image retrieval, using large language models (LLMs) for query rephrasing and preference-based reinforcement learning …