5 accepted papers
Reinforcement learning (RL) has achieved significant success across a wide range of domains, however, most existing methods are formulated in discrete time. In this work, we introduce a novel RL method for continuous-time control, where stochastic differential equations govern state-action dynamics.…
Pre-trained vision-language models (VLMs) have achieved remarkable success in image classification tasks, leveraging efficient prompt tuning methods. However, the reliability of fine-tuned VLMs in safety-critical scenarios remains a concern due to the under-explored issue of confidence calibration.…
Neural video compression (NVC) has made significant progress in recent years, while neural B-frame video compression (NBVC) remains underexplored compared to P-frame compression. NBVC can adopt bi-directional reference frames for better compression performance. However, NBVC's hierarchical coding ma…
Existing multimodal large model-based image compression frameworks often rely on a fragmented integration of semantic retrieval, latent compression, and generative models, resulting in suboptimal performance in both reconstruction fidelity and coding efficiency. To address these challenges, we propo…
AV1 is an emerging open-source and royalty-free video compression format as a successor to VP9.The increase in coding efficiency and complexity over VP9 is due to the time required to find the optimal partition structure among the more flexible encoding modes for the coding units (CUs) and predictio…