Search Results for author: XiaoJun Wu

Found 15 papers, 2 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

Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support

no code implementations26 Jan 2024 XiaoJun Wu, Dixiang Zhang, Ruyi Gan, Junyu Lu, Ziwei Wu, Renliang Sun, Jiaxing Zhang, Pingjian Zhang, Yan Song

Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support.

Language Modelling Text-to-Image Generation

Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects

no code implementations8 Dec 2023 Junyu Lu, Ruyi Gan, Dixiang Zhang, XiaoJun Wu, Ziwei Wu, Renliang Sun, Jiaxing Zhang, Pingjian Zhang, Yan Song

During the instruction fine-tuning stage, we introduce semantic-aware visual feature extraction, a crucial method that enables the model to extract informative features from concrete visual objects.

Image Captioning object-detection +5

iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design

no code implementations7 Dec 2023 Ruyi Gan, XiaoJun Wu, Junyu Lu, Yuanhe Tian, Dixiang Zhang, Ziwei Wu, Renliang Sun, Chang Liu, Jiaxing Zhang, Pingjian Zhang, Yan Song

However, there are few specialized models in certain domains, such as interior design, which is attributed to the complex textual descriptions and detailed visual elements inherent in design, alongside the necessity for adaptable resolution.

Image Generation

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 In-Context Learning +5

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 object-detection +3

Riemannian Multinomial Logistics Regression for SPD Neural Networks

1 code implementation18 May 2023 Ziheng Chen, Yue Song, Gaowen Liu, Ramana Rao Kompella, XiaoJun Wu, Nicu Sebe

Besides, our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks.

Action Recognition EEG +2

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 +3

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

Cannot find the paper you are looking for? You can Submit a new open access paper.