Author name cluster

Linwei Qiu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2026 Conference Paper

Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts

Linwei Qiu
Gongzhe Li
Xiaozhe Zhang
Qilin Sun
Fengying Xie

Image correction and rectangling are valuable tasks in practical photography systems such as smartphones. Recent remarkable advancements in deep learning have undeniably brought about substantial performance improvements in these fields. Nevertheless, existing methods mainly rely on task-specific architectures. This significantly restricts their generalization ability and effective application across a wide range of different tasks. In this paper, we introduce the Unified Rectification Framework (UniRect), a comprehensive approach that addresses these practical tasks from a consistent distortion rectification perspective. Our approach incorporates various task-specific inverse problems into a general distortion model by simulating different types of lenses. To handle diverse distortions, UniRect adopts one task-agnostic rectification framework with a dual-component structure: a Deformation Module, which utilizes a novel Residual Progressive Thin-Plate Spline (RP-TPS) model to address complex geometric deformations, and a subsequent Restoration Module, which employs Residual Mamba Blocks (RMBs) to counteract the degradation caused by the deformation process and enhance the fidelity of the output image. Moreover, a Sparse Mixture-of-Experts (SMoEs) structure is designed to circumvent heavy task competition in multi-task learning due to varying distortions. Extensive experiments demonstrate that our models have achieved state-of-the-art performance compared with other up-to-date methods.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection

Gongzhe Li
Linwei Qiu
Peibei Cao
Fengying Xie
Xiangyang Ji
Qilin Sun

High dynamic range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in autonomous driving. However, most neural networks for embedded vision are trained on low dynamic range (LDR) inputs and suffer substantial performance degradation when handling high-bit-depth HDR images due to the challenges posed by extreme dynamic ranges. In this paper, we propose a novel tone mapping method that not only bridges the gap between HDR RAW inputs and the LDR sRGB requirements of detection networks but also achieves end-to-end optimization with the downstream tasks. Instead of relying on traditional image signal processing (ISP) pipeline, we introduce neural photometric calibration to regularize dynamic ranges and a scaling-invariant local tone mapping module to preserve image details. In addition, our architecture also supports performance transfer finetuning, enabling efficient adaptation from the LDR model to the HDR RAW model with minimal cost. The proposed method outperforms traditional tone mapping algorithms and advanced AI-ISP methods in challenging automotive HDR scenes. Moreover, our pipeline achieves real-time processing of 4K high-bit-depth HDR inputs on the Nvidia Jetson platform.

PDF Details

JBHI Journal 2025 Journal Article

SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms

Yifei Chen
Zhu Zhu
Shenghao Zhu
Linwei Qiu
Binfeng Zou
Fan Jia
Yunpeng Zhu
Chenyan Zhang

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing high-dimensional microimage data. We propose a novel fine-grained classification model, SCKansformer, for bone marrow blood cells, which addresses these challenges and enhances classification accuracy and efficiency. The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer with the KAN, improving nonlinear feature representation and interpretability. The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances feature representation and reduces redundancy. The Global-Local Attention Encoder combines Multi-head Self-Attention with a Local Part module to capture both global and local features. We validated our model using the Bone Marrow Blood Cell Fine-Grained Classification Dataset (BMCD-FGCD), comprising over 10, 000 samples and nearly 40 classifications, developed with a partner hospital. Comparative experiments on our private dataset, as well as the publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer outperforms both typical and advanced microcell classification methods across all datasets.

Details DOI

IJCAI Conference 2022 Conference Paper

I2CNet: An Intra- and Inter-Class Context Information Fusion Network for Blastocyst Segmentation

Hua Wang
Linwei Qiu
Jingfei Hu
Jicong Zhang

The quality of a blastocyst directly determines the embryo's implantation potential, thus making it essential to objectively and accurately identify the blastocyst morphology. In this work, we propose an automatic framework named I2CNet to perform the blastocyst segmentation task in human embryo images. The I2CNet contains two components: IntrA-Class Context Module (IACCM) and InteR-Class Context Module (IRCCM). The IACCM aggregates the representations of specific areas sharing the same category for each pixel, where the categorized regions are learned under the supervision of the groundtruth. This aggregation decomposes a K-category recognition task into K recognition tasks of two labels while maintaining the ability of garnering intra-class features. In addition, the IRCCM is designed based on the blastocyst morphology to compensate for inter-class information which is gradually gathered from inside out. Meanwhile, a weighted mapping function is applied to facilitate edges of the inter classes and stimulate some hard samples. Eventually, the learned intra- and inter-class cues are integrated from coarse to fine, rendering sufficient information interaction and fusion between multi-scale features. Quantitative and qualitative experiments demonstrate that the superiority of our model compared with other representative methods. The I2CNet achieves accuracy of 94. 14% and Jaccard of 85. 25% on blastocyst public dataset.

PDF Details DOI