Search Results for author: Xiaoqi Li

Found 15 papers, 6 papers with code

Distribution-Aware Continual Test Time Adaptation for Semantic Segmentation

no code implementations24 Sep 2023 Jiayi Ni, Senqiao Yang, Jiaming Liu, Xiaoqi Li, Wenyu Jiao, ran Xu, Zehui Chen, Yi Liu, Shanghang Zhang

In this paper, we propose a distribution-aware tuning (DAT) method to make the semantic segmentation CTTA efficient and practical in real-world applications.

Autonomous Driving Semantic Segmentation

Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions

no code implementations20 Sep 2023 Yuxing Long, Xiaoqi Li, Wenzhe Cai, Hao Dong

The performances on the representative VLN task R2R show that our method surpasses the leading zero-shot VLN model by a large margin on all metrics.

Language Modelling Large Language Model

RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision

1 code implementation18 Sep 2023 Mingjie Pan, Jiaming Liu, Renrui Zhang, Peixiang Huang, Xiaoqi Li, Li Liu, Shanghang Zhang

3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels.

Autonomous Driving

An Overview of AI and Blockchain Integration for Privacy-Preserving

no code implementations6 May 2023 Zongwei Li, Dechao Kong, Yuanzheng Niu, Hongli Peng, Xiaoqi Li, Wenkai Li

In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.

De-identification Management +1

Exploring Sparse Visual Prompt for Cross-domain Semantic Segmentation

1 code implementation17 Mar 2023 Senqiao Yang, Jiarui Wu, Jiaming Liu, Xiaoqi Li, Qizhe Zhang, Mingjie Pan, Shanghang Zhang

Therefore, we propose a novel Sparse Visual Domain Prompts (SVDP) approach tailored for addressing domain shift problems in semantic segmentation, which holds minimal discrete trainable parameters (e. g. 10\%) of the prompt and reserves more spatial information.

Domain Adaptation Semantic Segmentation

Efficient Meta-Tuning for Content-aware Neural Video Delivery

1 code implementation20 Jul 2022 Xiaoqi Li, Jiaming Liu, Shizun Wang, Cheng Lyu, Ming Lu, Yurong Chen, Anbang Yao, Yandong Guo, Shanghang Zhang

Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications.


Adaptive Patch Exiting for Scalable Single Image Super-Resolution

1 code implementation22 Mar 2022 Shizun Wang, Jiaming Liu, Kaixin Chen, Xiaoqi Li, Ming Lu, Yandong Guo

Once the incremental capacity is below the threshold, the patch can exit at the specific layer.

Image Super-Resolution

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

no code implementations14 Feb 2022 Jian Wu, Wanli Liu, Chen Li, Tao Jiang, Islam Mohammad Shariful, Hongzan Sun, Xiaoqi Li, Xintong Li, Xinyu Huang, Marcin Grzegorzek

Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis.

Image Segmentation Semantic Segmentation

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

no code implementations21 Jan 2022 Xiaoqi Li, HaoYuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, Marcin Grzegorzek

In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly.

SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

1 code implementation30 Nov 2021 Shizun Wang, Ming Lu, Kaixin Chen, Jiaming Liu, Xiaoqi Li, Chuang Zhang, Ming Wu

However, existing methods mostly train the DNNs on uniformly sampled LR-HR patch pairs, which makes them fail to fully exploit informative patches within the image.

Data Augmentation Image Super-Resolution

CLUE: Towards Discovering Locked Cryptocurrencies in Ethereum

no code implementations2 Dec 2020 Xiaoqi Li, Ting Chen, Xiapu Luo, Chenxu Wang

Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by CLUE and repeating the same mistakes again can help users to save money.

Cryptography and Security

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