Search Results for author: XiaoJun Wu

Found 11 papers, 1 papers with code

Noise Learning for Text Classification: A Benchmark

no code implementations COLING 2022 Bo Liu, Wandi Xu, Yuejia Xiang, XiaoJun Wu, Lejian He, BoWen Zhang, Li Zhu

However, we find that noise learning in text classification is relatively underdeveloped: 1. many methods that have been proven effective in the image domain are not explored in text classification, 2. it is difficult to conduct a fair comparison between previous studies as they do experiments in different noise settings.

text-classification Text Classification

Ziya2: Data-centric Learning is All LLMs Need

no code implementations6 Nov 2023 Ruyi Gan, Ziwei Wu, Renliang Sun, Junyu Lu, XiaoJun Wu, Dixiang Zhang, Kunhao Pan, Ping Yang, Qi Yang, Jiaxing Zhang, Yan Song

Although many such issues are addressed along the line of research on LLMs, an important yet practical limitation is that many studies overly pursue enlarging model sizes without comprehensively analyzing and optimizing the use of pre-training data in their learning process, as well as appropriate organization and leveraging of such data in training LLMs under cost-effective settings.

Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning

no code implementations12 Oct 2023 Junyu Lu, Dixiang Zhang, XiaoJun Wu, Xinyu Gao, Ruyi Gan, Jiaxing Zhang, Yan Song, Pingjian Zhang

Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs.

Image Captioning Language Modelling +4

Edge Based Oriented Object Detection

no code implementations15 Sep 2023 Jianghu Shen, XiaoJun Wu

In the field of remote sensing, we often utilize oriented bounding boxes (OBB) to bound the objects.

object-detection Object Detection +2

Riemannian Multiclass Logistics Regression for SPD Neural Networks

no code implementations18 May 2023 Ziheng Chen, Yue Song, Gaowen Liu, Ramana Rao Kompella, XiaoJun Wu, Nicu Sebe

Moreover, we encompass the most popular classifier in existing SPD networks as a special case of our framework.


LabelPrompt: Effective Prompt-based Learning for Relation Classification

no code implementations16 Feb 2023 Wenjie Zhang, Xiaoning Song, ZhenHua Feng, Tianyang Xu, XiaoJun Wu

Specifically, associating natural language words that fill the masked token with semantic relation labels (\textit{e. g.} \textit{``org:founded\_by}'') is difficult.

Classification Contrastive Learning +2

Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning

no code implementations5 Nov 2022 Zhe Liu, Yun Li, Lina Yao, Xiaojun Chang, Wei Fang, XiaoJun Wu, Yi Yang

We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.

Compositional Zero-Shot Learning Disentanglement

PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion

no code implementations29 Jul 2021 Yu Fu, Tianyang Xu, XiaoJun Wu, Josef Kittler

In this paper, we propose a Patch Pyramid Transformer(PPT) to effectively address the above issues. Specifically, we first design a Patch Transformer to transform the image into a sequence of patches, where transformer encoding is performed for each patch to extract local representations.

Image Classification Image Reconstruction

a high efficiency fully convolutional networks for pixel wise surface defect detection

no code implementations journal 2019 Lingteng Qiu, XiaoJun Wu, ZHIYANG YU

Our method is composed of a segmentation stage (stage 1), a detection stage (stage 2), and a matting stage (stage 3).

Defect Detection Image Matting +1

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