Search Results for author: Xinxing Xu

Found 22 papers, 12 papers with code

Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning

1 code implementation3 Jan 2024 Zitong Huang, Ze Chen, Zhixing Chen, Erjin Zhou, Xinxing Xu, Rick Siow Mong Goh, Yong liu, ChunMei Feng, WangMeng Zuo

When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.

Few-Shot Class-Incremental Learning Incremental Learning +1

VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering

1 code implementation19 Dec 2023 Chun-Mei Feng, Yang Bai, Tao Luo, Zhen Li, Salman Khan, WangMeng Zuo, Xinxing Xu, Rick Siow Mong Goh, Yong liu

By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.

Image Retrieval Question Answering +2

Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation

no code implementations26 Nov 2023 Han Yuan, Chuan Hong, Nguyen Tuan Anh Tran, Xinxing Xu, Nan Liu

We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.

Lesion Segmentation Segmentation

Sentence-level Prompts Benefit Composed Image Retrieval

1 code implementation9 Oct 2023 Yang Bai, Xinxing Xu, Yong liu, Salman Khan, Fahad Khan, WangMeng Zuo, Rick Siow Mong Goh, Chun-Mei Feng

Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.

Attribute Composed Image Retrieval (CoIR) +2

DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume

1 code implementation14 Aug 2023 Xingyu Miao, Yang Bai, Haoran Duan, Yawen Huang, Fan Wan, Xinxing Xu, Yang Long, Yefeng Zheng

Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other.

Monocular Depth Estimation Optical Flow Estimation +1

Learning Federated Visual Prompt in Null Space for MRI Reconstruction

1 code implementation CVPR 2023 Chun-Mei Feng, Bangjun Li, Xinxing Xu, Yong liu, Huazhu Fu, WangMeng Zuo

Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.

MRI Reconstruction

Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging

no code implementations23 Mar 2023 Meng Wang, Lianyu Wang, Xinxing Xu, Ke Zou, Yiming Qian, Rick Siow Mong Goh, Yong liu, Huazhu Fu

Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation.

Federated Learning

Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment

no code implementations14 Mar 2023 Zhihao Chen, Yang Zhou, Anh Tran, Junting Zhao, Liang Wan, Gideon Ooi, Lionel Cheng, Choon Hua Thng, Xinxing Xu, Yong liu, Huazhu Fu

To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo).

Phrase Grounding Visual Grounding

CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow

1 code implementation CVPR 2022 Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong liu, Rick Goh, Hongyuan Zhu

This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images.

Optical Flow Estimation

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

Few-Shot Domain Adaptation with Polymorphic Transformers

1 code implementation10 Jul 2021 Shaohua Li, Xiuchao Sui, Jie Fu, Huazhu Fu, Xiangde Luo, Yangqin Feng, Xinxing Xu, Yong liu, Daniel Ting, Rick Siow Mong Goh

Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.

Domain Adaptation Segmentation

Crowd Counting With Partial Annotations in an Image

1 code implementation ICCV 2021 Yanyu Xu, Ziming Zhong, Dongze Lian, Jing Li, Zhengxin Li, Xinxing Xu, Shenghua Gao

To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i. e. only using partial annotations in each image as training data.

Active Learning Crowd Counting

Laplacian-Steered Neural Style Transfer

3 code implementations5 Jul 2017 Shaohua Li, Xinxing Xu, Liqiang Nie, Tat-Seng Chua

However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image.

Image Generation Style Transfer

Transfer Hashing with Privileged Information

no code implementations13 May 2016 Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng Qin, Rick Siow Mong Goh

Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+.

Quantization Transfer Learning

Simple and Efficient Learning using Privileged Information

no code implementations6 Apr 2016 Xinxing Xu, Joey Tianyi Zhou, IvorW. Tsang, Zheng Qin, Rick Siow Mong Goh, Yong liu

The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.

Image Categorization Test

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