2 citations · 6 across the 8 of their papers we have counts for
8 papers
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Xiaobo Xia, Xiaofeng Liu, Jiale Liu +5
Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality…
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
Yewen Li, Chaojie Wang, Xiaobo Xia +6
Unsupervised out-of-distribution (U-OOD) detection is to identify OOD data samples with a detector trained solely on unlabeled in-distribution (ID) data. The likelihood function es…
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception
Run Luo, Yunshui Li, Longze Chen +9
The development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs). While LMMs have achieved tremendous success by promoting…
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision
Zhaoqing Wang, Xiaobo Xia, Ziye Chen +4
Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is…
One-Shot Learning as Instruction Data Prospector for Large Language Models
Yunshui Li, Binyuan Hui, Xiaobo Xia +9
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may comp…
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang +5
Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such…
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
Xiaobo Xia, Jiale Liu, Shaokun Zhang +3
Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale…
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang +4
In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving stron…