6 accepted papers
Low-Rank Adaptation (LoRA) has become a standard paradigm for parameter-efficient fine-tuning (PEFT). However, its low-rank constraint can limit its capacity to express complex weight updates, leaving a performance gap compared with full fine-tuning. Existing extensions improve expressivity, but the
With the rapid development of large language models (LLMs), traditional full-parameter fine-tuning methods have become increasingly expensive in terms of computational resources and time costs. For this reason, parameter efficient fine-tuning (PEFT) methods have emerged. Among them, Low-Rank Adaptat…
Recently, we have witnessed a significant performance boosting for dialogue response selection task achieved by Cross-Encoder based models. However, such models directly feed the concatenation of context and response into the pre-trained model for interactive inference, ignoring the comprehensively…
Sentiment analysis has always been an important research direction in natural language processing. The research can be divided into explicit sentiment analysis and implicit sentiment analysis according to whether there are sentiment words in language expression. There have been many research results…
In this work, we present a solution to real-time monocular dense mapping. A tightly-coupled visual-inertial localization module is designed to provide metric and high-accuracy odometry. A motion stereo algorithm is proposed to take the video input from one camera to produce local depth measurements…
We address the important problem of achieving robust and easy-to-deploy visual state estimation for micro aerial vehicles (MAVs) operating in complex environments. We use a sensor suite consisting of multiple cameras and an IMU to maximize perceptual awareness of the surroundings and provide suffici…