8 accepted papers
This paper presents a model-based reinforcement learning (MBRL) approach with a receding horizon mechanism to optimize the lateral trajectory-tracking performance of autonomous vehicles (AVs). Accurate modeling of complex vehicle dynamics and adaptation to dynamic environments with limited data pose…
Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational efficiency, an essential factor for low-latency speech processing in real-time applications. In this paper, we propose
We present UGround, a **U**nified visual **Ground**ing paradigm that dynamically selects intermediate layers across **U**nrolled transformers as "mask as prompt'', diverging from the prevailing pipeline that leverages the fixed last hidden layer as "$\texttt{\}$ as prompt''. UGround addresses two pr…
In offline reinforcement learning, value overestimation caused by out-of-distribution (OOD) actions significantly limits policy performance. Recently, diffusion models have been leveraged for their strong distribution-matching capabilities, enforcing conservatism through behavior policy constraints.
The agile maneuvering control of autonomous vehicles (AVs) requires the tracking of reference trajectories characterized by high acceleration, sharp curvature, considerable disturbances, and significant time-varying, all while ensuring stability and accuracy. The inherent uncertainty and time-varyin
Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are threatened by targeted data poisoning and backdoor attacks due to massive training image-caption pairs crawled from the Internet. Previous defense methods correct poisoned image-caption pairs by matching a new c
Current Large Multimodal Models (LMMs) empowered visual grounding typically rely on \texttt <SEG> tokens as a text prompt to jointly optimize the vision-language model (e.g., LLaVA) and the downstream task-specific model (e.g., SAM). However, we observe that little research has looked into how it w…
Large language models (LLMs) have transformed code generation.However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts.Due to the lack of adequate benchmarks for Solidi