3 accepted papers
Large language models (LLMs) have shown significant promise in question-answering (QA) tasks, particularly in retrieval-augmented generation (RAG) scenarios and long-context applications. However, their performance is hindered by noisy reference documents, which often distract from essential informa…
Large language models (LLMs) achieve remarkable performance in code generation tasks. However, a significant performance disparity persists between popular programming languages (e.g., Python, C++) and others. To address this capability gap, we leverage the code translation task to train LLMs, ther…
This paper introduces a lightweight Semantic-guided Mutually Reinforcing network (SMR-Net) for the tasks of cross-modal image fusion and salient object detection (SOD). The core concept of SMR-Net is to leverage semantics for directing the mutual reinforcing between image fusion and SOD. Specificall…