2 citations · 5 across the 6 of their papers we have counts for
6 papers
Learning to Rewrite Prompts for Bootstrapping LLMs on Downstream Tasks
Qinhao Zhou, Xiang Xiang, Kun He +1
In recent years, the growing interest in Large Language Models (LLMs) has significantly advanced prompt engineering, transitioning from manual design to model-based optimization. P…
OpenHAIV: A Framework Towards Practical Open-World Learning
Xiang Xiang, Qinhao Zhou, Zhuo Xu +4
Substantial progress has been made in various techniques for open-world recognition. Out-of-distribution (OOD) detection methods can effectively distinguish between known and unkno…
OpenEarthSensing: Large-Scale Fine-Grained Benchmark for Open-World Remote Sensing
Xiang Xiang, Zhuo Xu, Yao Deng +7
The advancement of remote sensing, including satellite systems, facilitates the continuous acquisition of remote sensing imagery globally, introducing novel challenges for achievin…
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery
Qian Wan, Xiang Xiang, Qinhao Zhou
Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently. The challenge is how can a model detect novel classes and then incrementa…
Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning
Qinhao Zhou, Zihan Zhang, Xiang Xiang +3
Open-source pre-trained Large Language Models (LLMs) exhibit strong language understanding and generation capabilities, making them highly successful in a variety of tasks. However…
Continual Adapter Tuning with Semantic Shift Compensation for Class-Incremental Learning
Qinhao Zhou, Yuwen Tan, Boqing Gong +1
Class-incremental learning (CIL) aims to enable models to continuously learn new classes while overcoming catastrophic forgetting. The introduction of pre-trained models has brough…