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Peng Su

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6 papers

2 author rows

EAAI Journal 2025 Journal Article

A network with enhanced ability to interact scale features with channel features

Peng Su
Huizi Han
Mei Liu
Siqun Ma
Jiasheng Chen

Road damage must be accurately and promptly detected because road hazards have the potential to cause catastrophic traffic accidents. A Multiscale channel Shuffle Fusion-You Only Look Once(MSF-YOLO) series algorithm with multiscale cross-channel interaction data is suggested as a solution to this issue. It solves the problem of continuous change of road damage scale during vehicle traveling by enhancing the expressiveness of scale features and improves the network detection performance by enhancing the inter-channel interaction features so that the network learns the original input features as well as the input features after channel interaction with almost no additional computational cost. The experimental results show that Multiscale channel Shuffle Fusion-You Only Look Once-small (MSF-YOLO-s) accuracy on the Global Road Damage Detection Challenge (GRDDC) 2020 dataset is improved by 5. 8% to reach 65. 9%, while MSF-YOLO-s computation amount and number of parameters are reduced by 20. 9% and 8. 7%, respectively, compared to baseline. While experimental verification on the Common Objects in Context (COCO) 2017 datasets confirms its good generalizability. Ultimately, the model is implemented throughout the entire vehicle to anticipate fractures and enhance driving safety.

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AAAI Conference 2025 Conference Paper

Multi-view Granular-ball Contrastive Clustering

Peng Su
Shudong Huang
Weihong Ma
Deng Xiong
Jiancheng Lv

Previous multi-view contrastive learning methods typically operate at two scales: instance-level and cluster-level. The former generally constructs positive and negative pairs based on the correspondence between samples and view instances. These methods aim to bring positive pairs closer and push negative pairs further apart in the latent space. This kind of approaches has the drawback of inevitably introducing false negatives in an unsupervised setting, leading to reduced model discriminability. The latter usually involves calculating cluster assignments for samples under each view and maximizing view consensus by reducing distribution discrepancies through methods like optimizing the KL divergence between different view distributions or maximizing mutual information. However, clusters represent a macro structure that overlooks the local structure within the sample set, and the relationships between clusters across different views cannot be explicitly measured. To overcome the shortcomings of these two types of methods, we propose a method named Multi-view Granular-ball Contrastive Clustering (MGBCC). This method segments the sample set into coarse-grained granular balls, and establishes associations between intra-view and cross-view granular balls. These associations are reinforced in a shared latent space, thereby achieving multi-granularity contrastive learning. Granular balls lie between instances and clusters, naturally preserving the local topological structure of the sample set. We conduct extensive experiments to validate the effectiveness of the proposed method.

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ICML Conference 2024 Conference Paper

Multi-View Clustering by Inter-cluster Connectivity Guided Reward

Hao Dai
Yang Liu
Peng Su
Hecheng Cai
Shudong Huang
Jiancheng Lv 0001

Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true cluster number $\textit{K}$, which is rarely feasible in real-world scenarios. In this paper, we propose a novel graph-based multi-view clustering algorithm to infer unknown $\textit{K}$ through a graph consistency reward mechanism. To be specific, we evaluate the cluster indicator matrix during each iteration with respect to diverse $\textit{K}$. We formulate the inference process of unknown $\textit{K}$ as a parsimonious reinforcement learning paradigm, where the reward is measured by inter-cluster connectivity. As a result, our approach is capable of independently producing the final clustering result, free from the input of a predefined cluster number. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach in comparison to existing state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

Robust Contrastive Multi-view Kernel Clustering

Peng Su
Yixi Liu
Shujian Li
Shudong Huang
Jiancheng Lv

Multi-view kernel clustering (MKC) aims to fully reveal the consistency and complementarity of multiple views in a potential Hilbert space, thereby enhancing clustering performance. The clustering results of most MKC methods are highly sensitive to the quality of the constructed kernels, as traditional methods independently compute kernel matrices for each view without fully considering complementary information across views. In previous contrastive multi-view kernel learning, the goal was to bring cross-view instances of the same sample closer during the kernel construction process while pushing apart instances across samples to achieve a comprehensive integration of cross-view information. However, its inherent drawback is the potential inappropriate amplification of distances between different instances of the same clusters (i. e. , false negative pairs) during the training process, leading to a reduction in inter-class discriminability. To address this challenge, we propose a Robust Contrastive multi-view kernel Learning approach (R-CMK) against false negative pairs. It partitions negative pairs into different intervals based on distance or similarity, and for false negative pairs, reverses their optimization gradient. This effectively avoids further amplification of distances for false negative pairs while simultaneously pushing true negative pairs farther apart. We conducted comprehensive experiments on various MKC methods to validate the effectiveness of the proposed method. The code is available at https: //github. com/Duo-laimi/rcmk_main.

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AAAI Conference 2021 Conference Paper

Gradient Regularized Contrastive Learning for Continual Domain Adaptation

Shixiang Tang
Peng Su
Dapeng Chen
Wanli Ouyang

Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algorithms remains a challenge. To better understand this issue, we study the problem of continual domain adaptation, where the model is presented with a labelled source domain and a sequence of unlabelled target domains. The obstacles in this problem are both domain shift and catastrophic forgetting. We propose Gradient Regularized Contrastive Learning (GRCL) to solve the obstacles. At the core of our method, gradient regularization plays two key roles: (1) enforcing the gradient not to harm the discriminative ability of source features which can, in turn, benefit the adaptation ability of the model to target domains; (2) constraining the gradient not to increase the classification loss on old target domains, which enables the model to preserve the performance on old target domains when adapting to an in-coming target domain. Experiments on Digits, DomainNet and Office-Caltech benchmarks demonstrate the strong performance of our approach when compared to the state-of-the-art.

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YNIMG Journal 2019 Journal Article

Detection of neural connections with ex vivo MRI using a ferritin-encoding trans-synaptic virus

Ning Zheng
Peng Su
Yue Liu
Huadong Wang
Binbin Nie
Xiaohui Fang
Yue Xu
Kunzhang Lin

The elucidation of neural networks is essential to understanding the mechanisms of brain functions and brain disorders. Neurotropic virus-based trans-synaptic tracing tools have become an effective method for dissecting the structure and analyzing the function of neural-circuitry. However, these tracing systems rely on fluorescent signals, making it hard to visualize the panorama of the labeled networks in mammalian brain in vivo. One MRI method, Diffusion Tensor Imaging (DTI), is capable of imaging the networks of the whole brain in live animals but without information of anatomical connections through synapses. In this report, a chimeric gene coding for ferritin and enhanced green fluorescent protein (EGFP) was integrated into Vesicular stomatitis virus (VSV), a neurotropic virus that is able to spread anterogradely in synaptically connected networks. After the animal was injected with the recombinant VSV (rVSV), rVSV-Ferritin-EGFP, into the somatosensory cortex (SC) for four days, the labeled neural-network was visualized in the postmortem whole brain with a T2-weighted MRI sequence. The modified virus transmitted from SC to synaptically connected downstream regions. The results demonstrate that rVSV-Ferritin-EGFP could be used as a bimodal imaging vector for detecting synaptically connected neural-network with both ex vivo MRI and fluorescent imaging. The strategy in the current study has the potential to longitudinally monitor the global structure of a given neural-network in living animals.

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