Author name cluster

Quansen Sun

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5 papers

1 author row

AAAI Conference 2025 Conference Paper

TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering

Zhongwen Wang
Xingfeng Li
Yinghui Sun
Quansen Sun
Yuan Sun
Han Ling
Jian Dai
Zhenwen Ren

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods.

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EAAI Journal 2024 Journal Article

CSANet: Cross-self attention guided by semantic click embedding for interactive segmentation

Zongyuan Ding
Hongyuan Wang
Quansen Sun
Tao Wang
Fuhua Chen

In click-based deep interactive segmentation, click encoding and fusion with multi-scale features are vital for manipulating segmentation performance. Existing click encoding methods only incorporate position priors but lack semantics, leading to unstable interaction efficiency. Meanwhile, in order to fuse multi-scale features, current methods extract these features at the abstract semantic level but neglect the constraints imposed by detailed information on semantic features. This oversight makes the network prone to over-segmentation. To address these challenges, we propose a cross-self attention guided by semantic click embedding for interactive segmentation. First, we build semantic click embeddings from the semantic features by embedding positive clicks into continuous connected semantic regions while preserving the role of correction for negative clicks. This enriches the semantic priors for appropriate clicks. Next, we utilize the self-attention mechanism to leverage both detailed and semantic features of the network, constructing a cross-attention mechanism that suppresses the over-segmentation phenomenon. Finally, the semantic click embedding is utilized to weight the affinity matrix of the attention mechanism, ensuring that long-distance dependencies are only relevant to the target of interest. Comprehensive experiments prove that our approach improves interaction efficiency and achieves state-of-the-art performance on public datasets.

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IJCAI Conference 2024 Conference Paper

Fast Unpaired Multi-view Clustering

Xingfeng Li
Yuangang Pan
Yinghui Sun
Quansen Sun
Ivor Tsang
Zhenwen Ren

Anchor based pair-wised multi-view clustering often assumes multi-view data are paired, and has demonstrated significant advancements in recent years. However, this presumption is easily violated, and data is commonly unpaired fully in practical applications due to the influence of data collection and storage processes. Addressing unpaired large-scale multi-view data through anchor learning remains a research gap. The absence of pairing in multi-view data disrupts the consistency and complementarity of multiple views, posing significant challenges in learning powerful and meaningful anchors and bipartite graphs from unpaired multi-view data. To tackle this challenge, this study proposes a novel Fast Unpaired Multi-view Clustering (FUMC) framework for fully unpaired large-scale multi-view data. Specifically, FUMC first designs an inverse local manifold learning paradigm to guide the learned anchors for effective pairing and balancing, ensuring alignment, fairness, and power in unpaired multi-view data. Meanwhile, a novel bipartite graph matching framework is developed to align unpaired bipartite graphs, creating a consistent bipartite graph from unpaired multi-view data. The efficacy, efficiency, and superiority of our FUMC are corroborated through extensive evaluations on numerous benchmark datasets with shallow and deep SOTA methods.

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AAAI Conference 2021 Conference Paper

Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning

Zhenwen Ren
Quansen Sun
Dong Wei

Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple kernel clustering (MKC), which have both been widely used to identify clusters that are non-linearly separable. However, both of them have their own shortcomings: 1) the KKM-based methods usually focus on learning a discrete clustering indicator matrix via a combined consensus kernel, but cannot exploit the high-order affinities of all pre-defined base kernels; and 2) the SC-based methods require a robust and meaningful affinity graph in kernel space as input in order to form clusters with desired clustering structure. In this paper, a novel method, kernel k-means coupled graph tensor (KCGT), is proposed to graciously couple KKM and SC for seizing their merits and evading their demerits simultaneously. In specific, we innovatively develop a new graph learning paradigm by leveraging an explicit theoretical connection between clustering indicator matrix and affinity graph, such that the affinity graph propagated from KKM enjoys the valuable block diagonal and sparse property. Then, by using this graph learning paradigm, base kernels can produce multiple candidate affinity graphs, which are stacked into a low-rank graph tensor for capturing the highorder affinity of all these graphs. After that, by averaging all the frontal slices of the tensor, a high-quality affinity graph is obtained. Extensive experiments have shown the superiority of KCGT compared with the state-of-the-art MKC methods.

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IJCAI Conference 2017 Conference Paper

Interactive Image Segmentation via Pairwise Likelihood Learning

Tao Wang
Quansen Sun
Qi Ge
Zexuan Ji
Qiang Chen
Guiyu Xia

This paper presents an interactive image segmentation approach where the segmentation problem is formulated as a probabilistic estimation manner. Instead of measuring the distances between unseeded pixels and seeded pixels, we measure the similarities between pixel pairs and seed pairs to improve the robustness to the seeds. The unary prior probability of each pixel belonging to the foreground F and background B can be effectively estimated based on the similarities with label pairs (F, F), (F, B), (B, F) and (B, B). Then a likelihood learning framework is proposed to fuse the region and boundary information of the image by imposing the smoothing constraint on the unary potentials. Experiments on challenging data sets demonstrate that the proposed method can obtain better performance than state-of-the-art methods.

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