Ce Zhu

YNIMG Journal 2025 Journal Article

DeepNuParc: A novel deep clustering framework for fine-scale parcellation of brain nuclei using diffusion MRI tractography

• Haolin He
• Ce Zhu
• Le Zhang
• Yipeng Liu
• Xiao Xu
• Yuqian Chen
• Leo Zekelman
• Jarrett Rushmore

Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation of the brain nuclei is vital for a comprehensive understanding of their anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying their subdivisions. In this work, we present a deep clustering pipeline, namely DeepNuParc, to perform automated, fine-scale parcellation of brain nuclei using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the nuclei of interest directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the nuclei. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable nuclei parcellation at a finer scale. We demonstrate DeepNuParc on two important brain structures, i.e. the amygdala and the thalamus, that are known to have multiple anatomically and functionally distinct nucleus subdivisions. Experimental results show that DeepNuParc enables consistent parcellation of the nuclei into multiple parcels across multiple subjects and achieves good correspondence with the widely used coarse-scale atlases. Our code is available at https://github.com/HarlandZZC/deep_nuclei_parcellation.

NeurIPS Conference 2025 Conference Paper

KaRF: Weakly-Supervised Kolmogorov-Arnold Networks-based Radiance Fields for Local Color Editing

• Wudi Chen
• Zhiyuan Zha
• Shigang Wang
• Bihan Wen
• Xin Yuan
• Jiantao Zhou
• Zipei Fan
• Gang Yan

Recent advancements have suggested that neural radiance fields (NeRFs) show great potential in color editing within the 3D domain. However, most existing NeRF-based editing methods continue to face significant challenges in local region editing, which usually lead to imprecise local object boundaries, difficulties in maintaining multi-view consistency, and over-reliance on annotated data. To address these limitations, in this paper, we propose a novel weakly-supervised method called KaRF for local color editing, which facilitates high-fidelity and realistic appearance edits in arbitrary regions of 3D scenes. At the core of the proposed KaRF approach is a unified two-stage Kolmogorov-Arnold Networks (KANs)-based radiance fields framework, comprising a segmentation stage followed by a local recoloring stage. This architecture seamlessly integrates geometric priors from NeRF to achieve weakly-supervised learning, leading to superior performance. More specifically, we propose a residual adaptive gating KAN structure, which integrates KAN with residual connections, adaptive parameters, and gating mechanisms to effectively enhance segmentation accuracy and refine specific editing effects. Additionally, we propose a palette-adaptive reconstruction loss, which can enhance the accuracy of additive mixing results. Extensive experiments demonstrate that the proposed KaRF algorithm significantly outperforms many state-of-the-art methods both qualitatively and quantitatively. Our code and more results are available at: https: //github. com/PaiDii/KARF. git.

NeurIPS Conference 2025 Conference Paper

Multimodal Causal Reasoning for UAV Object Detection

• Nianxin Li
• Mao Ye
• Lihua Zhou
• Shuaifeng Li
• Song Tang
• Luping Ji
• Ce Zhu

Unmanned Aerial Vehicle (UAV) object detection faces significant challenges due to complex environmental conditions and different imaging conditions. These factors introduce significant changes in scale and appearance, particularly for small objects that occupy limited pixels and exhibit limited information, complicating detection tasks. To address these challenges, we propose a Multimodel Causal Reasoning framework based on YOLO backbone for UAV Object Detection (MCR-UOD). The key idea is to use the backdoor adjustment to discover the condition-invariant object representation for easy detection. Specifically, the YOLO backbone is first adjusted to incorporate the pre-trained vision-language model. The original category labels are replaced with semantic text prompts, and the detection head is replaced with text-image contrastive learning. Based on this backbone, our method consists of two parts. The first part, named language guided region exploration, discovers the regions with high probability of object existence using text embeddings based on vision-language model such as CLIP. Another part is the backdoor adjustment casual reasoning module, which constructs a confounder dictionary tailored to different imaging conditions to capture global image semantics and derives a prior probability distribution of shooting conditions. During causal inference, we use the confounder dictionary and the prior to intervene on local instance features, disentangling condition variations, and obtaining condition-invariant representations. Experimental results on several public datasets confirm the state-of-the-art performance of our approach. The code, data and models will be released upon publication of this paper.

AAAI Conference 2025 Conference Paper

SLR-MVTC: Smooth Low-Rank Multi-View Tensor Clustering

• Zhen Long
• Yipeng Liu
• Yazhou Ren
• Ce Zhu

Multi-view tensor clustering (MVTC) has gained much attention for its effectiveness in capturing global high-order correlations across views. However, current MVTC methods suffer from two limitations: 1) adopting a two-stage process to learn the latent features for clustering, and 2) either ignoring local similarities within views or treating local similarities and global high-order correlations equally. In this paper, we propose a smooth low-rank MVTC (SLR-MVTC) method, which aims to extract latent features that are smooth within each view and low-rank across views, enhancing clustering performance. Specifically, we first learn latent features from each view using orthogonal projection and then construct the latent feature tensor by concatenation and rotation. Then, we introduce a new smooth tensor nuclear norm to depict the low-rank components of the low-frequency parts in the feature tensor. Benefiting from the fast Fourier transform along the sample dimension, the obtained low-frequency components effectively capture local smoothness within views, while their low-rank parts further explore global correlations across views. Experimental results on six multi-view datasets demonstrate that SLR-MVTC outperforms state-of-the-art algorithms in terms of clustering performance and CPU time.

AAAI Conference 2025 Conference Paper

WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network

• Zhendong Liu
• Le Zhang
• Bing Li
• Yingjie Zhou
• Zhenghua Chen
• Ce Zhu

We address the challenge of WiFi-based temporal activity detection and propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantic Encoders and Local Sensitive Response Encoders. The Temporal Signal Semantic Encoder splits feature learning into high and low-frequency components, using a novel Signed Mask-Attention mechanism to emphasize important areas and downplay unimportant ones, with the features fused using ContraNorm. The Local Sensitive Response Encoder captures fluctuations without learning. These feature pyramids are then combined using a new cross-attention fusion mechanism. We also introduce a dataset with over 2,114 activity segments across 553 WiFi CSI samples, each lasting around 85 seconds. Extensive experiments show our method outperforms challenging baselines.

NeurIPS Conference 2024 Conference Paper

Causal Context Adjustment Loss for Learned Image Compression

• Minghao Han
• Shiyin Jiang
• Shengxi Li
• Xin Deng
• Mai Xu
• Ce Zhu
• Shuhang Gu

In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the decoded causal context. However, extant methods are highly dependent on the fixed hand-crafted causal context. The question of how to guide the auto-encoder to generate a more effective causal context benefit for the autoregressive entropy models is worth exploring. In this paper, we make the first attempt in investigating the way to explicitly adjust the causal context with our proposed Causal Context Adjustment loss (CCA-loss). By imposing the CCA-loss, we enable the neural network to spontaneously adjust important information into the early stage of the autoregressive entropy model. Furthermore, as transformer technology develops remarkably, variants of which have been adopted by many state-of-the-art (SOTA) LIC techniques. The existing computing devices have not adapted the calculation of the attention mechanism well, which leads to a burden on computation quantity and inference latency. To overcome it, we establish a convolutional neural network (CNN) image compression model and adopt the unevenly channel-wise grouped strategy for high efficiency. Ultimately, the proposed CNN-based LIC network trained with our Causal Context Adjustment loss attains a great trade-off between inference latency and rate-distortion performance.

TMLR Journal 2023 Journal Article

Scalable Deep Compressive Sensing

• Zhonghao Zhang
• Yipeng Liu
• Xingyu Cao
• Fei Wen
• Ce Zhu

Deep learning has been used to image compressive sensing (CS) for enhanced reconstruction performance. However, most existing deep learning methods train different models for different subsampling ratios, which brings an additional hardware burden. In this paper, we develop a general framework named scalable deep compressive sensing (SDCS) for the scalable sampling and reconstruction (SSR) of all existing end-to-end-trained models. In the proposed way, images are measured and initialized linearly. Two sampling matrix masks are introduced to flexibly control the subsampling ratios used in sampling and reconstruction, respectively. To achieve a reconstruction model with flexible subsampling ratios, a training strategy dubbed scalable training is developed. In scalable training, the model is trained with the sampling matrix and the initialization matrix at various subsampling ratios by integrating different sampling matrix masks. Experimental results show that models with SDCS can achieve SSR without changing their structure while maintaining good performance, and SDCS outperforms other SSR methods.

EAAI Journal 2023 Journal Article

Single-image HDR reconstruction by dual learning the camera imaging process

• Lei She
• Mao Ye
• Shuai Li
• Yu Zhao
• Ce Zhu
• Hu Wang

IJCAI Conference 2022 Conference Paper

KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction

• Hu Wang
• Mao Ye
• Xiatian Zhu
• Shuai Li
• Ce Zhu
• Xue Li

Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constraining solution space or just simply imitate the inverse camera imaging pipeline in stages, without directly formulating the HDR image generation process. In this work, we address this problem by integrating LDR-to-HDR imaging knowledge into an UNet architecture, dubbed as Knowledge-inspired UNet (KUNet). The conversion from LDR-to-HDR image is mathematically formulated, and can be conceptually divided into recovering missing details, adjusting imaging parameters and reducing imaging noise. Accordingly, we develop a basic knowledge-inspired block (KIB) including three subnetworks corresponding to the three procedures in this HDR imaging process. The KIB blocks are cascaded in the similar way to the UNet to construct HDR image with rich global information. In addition, we also propose a knowledge inspired jump-connect structure to fit a dynamic range gap between HDR and LDR images. Experimental results demonstrate that the proposed KUNet achieves superior performance compared with the state-of-the-art methods. The code, dataset and appendix materials are available at https: //github. com/wanghu178/KUNet. git.

EAAI Journal 2015 Journal Article

Fast crowd density estimation with convolutional neural networks

• Min Fu
• Pei Xu
• Xudong Li
• Qihe Liu
• Mao Ye
• Ce Zhu