Search Results for author: Ziqian Lu

Found 7 papers, 4 papers with code

MediSee: Reasoning-based Pixel-level Perception in Medical Images

no code implementations15 Apr 2025 Qinyue Tong, Ziqian Lu, Jun Liu, Yangming Zheng, Zheming Lu

In this paper, we introduce a novel medical vision task: Medical Reasoning Segmentation and Detection (MedSD), which aims to comprehend implicit queries about medical images and generate the corresponding segmentation mask and bounding box for the target object.

Logical Reasoning Reasoning Segmentation +1

Improving Skeleton-based Action Recognition with Interactive Object Information

1 code implementation9 Jan 2025 Hao Wen, Ziqian Lu, Fengli Shen, Zhe-Ming Lu, Jialin Cui

We propose a new action recognition framework introducing object nodes to supplement absent interactive object information.

Action Recognition Data Augmentation +3

Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning

no code implementations22 Aug 2024 Mushui Liu, Fangtai Wu, Bozheng Li, Ziqian Lu, Yunlong Yu, Xi Li

Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples.

Few-Shot Learning

CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation

1 code implementation17 May 2024 Mushui Liu, Jun Dan, Ziqian Lu, Yunlong Yu, Yingming Li, Xi Li

In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images.

Decoder Mamba +2

SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios

1 code implementation6 Dec 2023 Mushui Liu, Weijie He, Ziqian Lu, Yunlong Yu

Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.

Prompt-based test-time real image dehazing: a novel pipeline

1 code implementation29 Sep 2023 Zixuan Chen, Zewei He, Ziqian Lu, Xuecheng Sun, Zhe-Ming Lu

Accordingly, we first apply a prompt generation module (PGM) to generate a visual prompt, which is the reference of appropriate statistical perturbations for mean and standard deviation.

Image Dehazing

Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization

no code implementations28 Sep 2023 Zewei He, Zixuan Chen, Ziqian Lu, Xuecheng Sun, Zhe-Ming Lu

Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.

Image Dehazing

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