Author name cluster

Runze Zhang

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Opinion Maximization in Social Networks by Modifying Internal Opinions

Gengyu Wang
Runze Zhang
Zhongzhi Zhang

Public opinion governance in social networks is critical for public health campaigns, political elections, and commercial marketing. In this paper, we addresse the problem of maximizing overall opinion in social networks by strategically modifying the internal opinions of key nodes. Traditional matrix inversion methods suffer from prohibitively high computational costs, prompting us to propose two efficient sampling-based algorithms. Furthermore, we develop a deterministic asynchronous algorithm that exactly identifies the optimal set of nodes through asynchronous update operations and progressive refinement, ensuring both efficiency and precision. Extensive experiments on real-world datasets demonstrate that our methods outperform baseline approaches. Notably, our asynchronous algorithm delivers exceptional efficiency and accuracy across all scenarios, even in networks with tens of millions of nodes.

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EAAI Journal 2025 Journal Article

SSmokeDet: A novel network dedicated to small-scale smoke detection

Jingjing Wang
Li Wang
Runze Zhang
Xiaochuan Li
Baoyu Fan

Small smoke detection is essential for the warning of early and distant smoke. However, small-scale smoke occupies few pixels and only provides limited semantic information, causing a considerable challenge for its detection. To this end, we propose a novel network dedicated to small-scale smoke detection (SSmokeDet). Firstly, we put forward a small-net (SNet) backbone to control the receptive field of the model, which facilitates a better observation of the small smoke. Secondly, combined with a residual connection, a multiple spatial pyramid pooling (MultiSPP) is designed to compensate for the lack of small smoke information on the high level by contextual information reinforcement. Lastly, a self-cooperation head (SCHead) is devised for cross-layer communication after refining branching features at different scales. Moreover, an anchor-free mechanism is employed to break the size limitation of predefined anchor boxes and decode the smoke location information directly for the small-scale smoke detection task. Extensive experiments are conducted on both self-made and synthetic databases with various scenes, and the results demonstrate that our SSmokeDet is superior to the state-of-the-art methods. Compared with the baseline, the accuracy of small-scale smoke is effectively improved by 10. 2%, and the average precision is increased by 4. 9%.

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AAAI Conference 2024 Conference Paper

Image Content Generation with Causal Reasoning

Xiaochuan Li
Baoyu Fan
Runze Zhang
Liang Jin
Di Wang
Zhenhua Guo
Yaqian Zhao
Rengang Li

The emergence of ChatGPT has once again sparked research in generative artificial intelligence (GAI). While people have been amazed by the generated results, they have also noticed the reasoning potential reflected in the generated textual content. However, this current ability for causal reasoning is primarily limited to the domain of language generation, such as in models like GPT-3. In visual modality, there is currently no equivalent research. Considering causal reasoning in visual content generation is significant. This is because visual information contains infinite granularity. Particularly, images can provide more intuitive and specific demonstrations for certain reasoning tasks, especially when compared to coarse-grained text. Hence, we propose a new image generation task called visual question answering with image (VQAI) and establish a dataset of the same name based on the classic Tom and Jerry animated series. Additionally, we develop a new paradigm for image generation to tackle the challenges of this task. Finally, we perform extensive experiments and analyses, including visualizations of the generated content and discussions on the potentials and limitations. The code and data are publicly available under the license of CC BY-NC-SA 4.0 for academic and non-commercial usage at: https://github.com/IEIT-AGI/MIX-Shannon/blob/main/projects/VQAI/lgd_vqai.md.

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AAAI Conference 2023 Conference Paper

JR2Net: Joint Monocular 3D Face Reconstruction and Reenactment

Jiaxiang Shang
Yu Zeng
Xin Qiao
Xin Wang
Runze Zhang
Guangyuan Sun
Vishal Patel
Hongbo Fu

Face reenactment and reconstruction benefit various applications in self-media, VR, etc. Recent face reenactment methods use 2D facial landmarks to implicitly retarget facial expressions and poses from driving videos to source images, while they suffer from pose and expression preservation issues for cross-identity scenarios, i.e., when the source and the driving subjects are different. Current self-supervised face reconstruction methods also demonstrate impressive results. However, these methods do not handle large expressions well, since their training data lacks samples of large expressions, and 2D facial attributes are inaccurate on such samples. To mitigate the above problems, we propose to explore the inner connection between the two tasks, i.e., using face reconstruction to provide sufficient 3D information for reenactment, and synthesizing videos paired with captured face model parameters through face reenactment to enhance the expression module of face reconstruction. In particular, we propose a novel cascade framework named JR2Net for Joint Face Reconstruction and Reenactment, which begins with the training of a coarse reconstruction network, followed by a 3D-aware face reenactment network based on the coarse reconstruction results. In the end, we train an expression tracking network based on our synthesized videos composed by image-face model parameter pairs. Such an expression tracking network can further enhance the coarse face reconstruction. Extensive experiments show that our JR2Net outperforms the state-of-the-art methods on several face reconstruction and reenactment benchmarks.

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ICRA Conference 2019 Conference Paper

Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation

Tianwei Shen
Zixin Luo
Lei Zhou 0011
Hanyu Deng
Runze Zhang
Tian Fang
Long Quan

Accurate relative pose is one of the key components in visual odometry (VO) and simultaneous localization and mapping (SLAM). Recently, the self-supervised learning framework that jointly optimizes the relative pose and target image depth has attracted the attention of the community. Previous works rely on the photometric error generated from depths and poses between adjacent frames, which contains large systematic error under realistic scenes due to reflective surfaces and occlusions. In this paper, we bridge the gap between geometric loss and photometric loss by introducing the matching loss constrained by epipolar geometry in a self-supervised framework. Evaluated on the KITTI dataset, our method outperforms the state-of-the-art unsupervised egomotion estimation methods by a large margin. The code and data are available at https://github.com/hlzz/DeepMatchVO.

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