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I am an undergraduate student at Sichuan University(SCU),majoring in Computer Science & technology. My Major GPA (CS cources): 3.79/4, 89.39/100; Overall GPA: 3.78/4, 89.25/100 In the Sichuan University, I am working as a research assistant at MachineILab since 2022, advised by Prof. JiZhe Zhou. I am participating in one National Natural Science Foundation of China and one National Key R&D Program of China. 🌟Exciting: I am about to go to the Hong Kong Polytechnic University to pursue a PhD degree. My supervisors are Qing Li and Wenqi Fan. Email / CV / Google Scholar / Github / Zhihu |
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My research areas include recommendation systems(RS), large language models(LLM), computer vision(CV), and graph neural networks(GNN). My previous research was primarily focused on topics within computer vision, such as tampering detection and object recognition tasks. Currently, I am diving into the realms of large language models(LLM) and recommendation systems(RS). |
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2024-09-01 - beginning my pursuit of a PhD degree. 2024-06-26 - graduated from Sichuan University with a bachelor's degree. 2024-06-12 - completed the co-work project IMDLBenCo and finish a paper IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization 2024-05-24 - finish a paper Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning |
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Xiaochen Ma, Xuekang Zhu, Lei Su, Bo Du, Zhuohang Jiang, Bingkui Tong, Zeyu Lei, Xinyu Yang, Chi-Man Pun, Jiancheng Lv, Jizhe Zhou A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo:i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. |
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Zhuohang Jiang, Bingkui Tong, Xia Du, Ahmed Alhammadi, Jizhe Zhou To explicitly derive the objects' privacy class from the scene contexts, in this paper, we interpret the POI task as a visual reasoning task aimed at the privacy of each object in the scene. Following this interpretation, we propose the PrivacyGuard framework for POI. PrivacyGuard contains three stages. i) Structuring: an unstructured image is first converted into a structured, heterogeneous scene graph that embeds rich scene contexts. ii) Data Augmentation: a contextual perturbation oversampling strategy is proposed to create slightly perturbed privacy-sensitive objects in a scene graph, thereby balancing the skewed distribution of privacy classes. iii) Hybrid Graph Generation & Reasoning: the balanced, heterogeneous scene graph is then transformed into a hybrid graph by endowing it with extra "node-node" and "edge-edge" homogeneous paths. These homogeneous paths allow direct message passing between nodes or edges, thereby accelerating reasoning and facilitating the capturing of subtle context changes. |
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Xiaochen Ma, Bo Du,Zhuohang Jiang, Ahmed Y. Al Hammadi, Jizhe Zhou Due to limited datasets, there is currently no pure ViT-based approach for IML to serve as a benchmark, and CNNs dominate the entire task. Nevertheless, CNNs suffer from weak long-range and non-semantic modeling. To bridge this gap, based on the fact that artifacts are sensitive to image resolution, amplified under multi-scale features, and massive at the manipulation border, we formulate the answer to the former question as building a ViT with high-resolution capacity, multi-scale feature extraction capability, and manipulation edge supervision that could converge with a small amount of data. We term this simple but effective ViT paradigm IML-ViT, which has significant potential to become a new benchmark for IML. Extensive experiments on five benchmark datasets verified our model outperforms the state-of-the-art manipulation localization methods. |
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Xiaochen Ma, Zhuohang Jiang, Xiong Xu, Chi-Man Pun, Jizhe Zhou This necessitates IML models to carry out a semantic understanding of the entire image. In this paper, we reformulate the IML task as a high‑level vision task that greatly benefits from low‑level features. We propose a method to enhance the Masked Autoencoder (MAE) by incorporating high‑resolution inputs and a perceptual loss supervision module, which we term Perceptual MAE (PMAE). While MAE has demonstrated an impressive understanding of object semantics, PMAE can also comprehend low‑level semantics with our proposed enhancements. This paradigm effectively unites the low‑level and high‑level features of the IML task and outperforms state‑of‑the‑art tampering localization methods on five publicly available datasets, as evidenced by extensive experiments. |
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Qin Li, Chunfang Yu, Zhuohang Jiang, AND Jizhe Zhou We propose a novel Contour‑aware Contrastive Learning Network (CaCL‑Net) based on the encoder‑decoder architecture. On the encoder side, since the contour is foremost concerned in IML, we consider the image patches sampled along the manipulation contour are the hard examples and set them as the anchor. The patches of pure tampered and authentic pixels are set as positives and negatives respectively to conduct contrastive learning. The decoder then manages to specify the manipulated regions and restores the explicit contours of the manipulations through the proposed Contour Binary Cross‑Entropy (CBCE) loss. |
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Advisor: Jizhe Zhou Participate as an intern National Natural Science Foundation of China , 2024 The privacy-sensitive object detection problem reauires the model to locate private objects in bounding boxes on images or videos. Research on privacy-sensitive object detection has imnortant value for personal-privacyprotection. Privacy-sensitive ob ject detection is actually a scene reasoning problem. However existing privacy-sensitive oh iect detection methods are all basedon the object detection framework.Due to the lack of scene reasoning ability,existing methods suffer from detection accuracy,generalizability,and interpretability.This project intends to build a set of privacy-sensitive objectdetection methods with scene reasoning capability through scene graphs. Unlikeother tasks, privacy-sensitive object detection requires a non-parametric scenegraph structure to keep the graph sparse，dynamic，and interpretable.Therefore,this project correspondingly proposes the scene graph structure learning methods.By studying 1) the distillation method of the graph structure to sparse the scenegraph，2) the transferring method between the scene graphs of different frames tomake the scene graph dynamic，3) the privacy-rule reasoning method based on thescene graph structure，solves the problem of scene graph generation with thenon-parametric graph structure， builds a new privacy-sensitive object detectionframework based on scene reasoning， break the bottlenecks of privacy-sensitiveobject detection methods in accuracy，generalizability,and interpretability.Thisprivacy-sensitive object detection framework also enriched the theoretical framework and application scenarios of neural networks. |
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Advisor: Jizhe Zhou Participate as an intern National Key R&D Program of China, 2023 This topic proposes to adopt the scheme of "first completing information, then path reasoning". Specifically, this sub project intends to improve the initial network diagram based on the data base established in previous projects and further consider the frequency of common occurrence of impact factors. Then, based on the external knowledge causal information completion method of remote supervision, the network graph is again completed and cleaned, and then a path reasoning algorithm based on depth first traversal of the graph is used to achieve feedforward reasoning. Finally, a human-computer interactive network information verification method based on uncertainty reasoning is used to revise the causal relationship in the network diagram again based on artificial feedback on the reasoning path, further improving the accuracy and recall rate of the path reasoning algorithm results. |
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Sichuan University, Chengdu, Sichuan, China
B.E. in Computer Science and Technology • Sep. 2020 to Jun. 2024 |
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Hong Kong Polytechnic University, Hongkong, China
PHD. in Computer Science and Technology • Sep. 2024 to Present |
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DICALab, Sichuan University
Research Assistant • Sep. 2022 to Jun. 2024 Advisor: Prof. JiZhe Zhou |
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Covariant association, Sichuan University
the president of the covariant association • Sep. 2022 to Jun. 2023 the Covariant association obtain at least 400+ members, in which communicate the technology of the computer science. |
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| Computer Design Competition China, 2023 | Provincial First Prize |
| Comprehensive First Class Scholarship Sichuan University, Sichuan, China, 2022 | Top 1% |
| Outstanding students of Sichuan University Sichuan University, Sichuan, China, 2022 | Top 5% |
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Updated at Jun. 2024
Thanks Jon Barron for this amazing template
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