Accurate localization of oriented objects in remote sensing images (RSI) faces complex challenges, particularly the difficulty of feature modeling arising from the geometric complexity of oriented targets, manifested as arbitrary orientations and scale variations. Existing paradigms, primarily based on Convolutional Neural Networks (CNNs) or Transformers, which struggle to capture robust rotation-invariant features due to inherent structural deficiencies (feature confusion due to fixed grid sampling in CNNs, spatial misalignment due to inadequate orientation encoding in attention mechanism). Graph-based feature modeling displays robustness against geometric complexity, prompting several studies to explore the incorporation of graph structures. However, these methods primarily focus on region proposals or pixel-level rotation correlations, struggling to effectively model the rotation-invariant features of targets. In this paper, we propose the Graph-based Semantic Rerouting Interaction (GSRI), shifting the modeling of rotated semantic representation for objects from tilted spatial-domain pixel recognition to the exploration of consistent channel-level semantic responses, thereby achieving robust rotation semantic feature modeling. Specifically, GSRI introduces a dynamic dilated KNN based graph structure to enhance the rotation-invariant feature modeling through capturing the consistent activation relationships of similar targets in high-dimensional feature space. By incorporating a cross-attention mechanism with dual positional encoding supervision, GSRI facilitates the alignment and propagation of robust rotational semantics across cross-scale feature maps, thereby mitigating background noise and bolstering the perceptibility of rotated objects in complex RSI scenarios. Extensive experiments on the DOTA-v1.0 and DIOR-R datasets demonstrate the superior effectiveness of our method.

Relation reasoning in temporal knowledge graph infers unknown or emerging relational dependencies from historical structured data. Traditional approaches face inherent limitations in capturing complex semantic correlations and structural patterns among relations. To tackle this problem, we propose the Relational Multi-path Enhancement network (RME), which primarily focuses on relation modeling to enrich relation representations through comprehensive multi-path analysis. RME consists of five key components: (1) Controlled random walk module creates multi-hop head-to-tail paths using an adaptive stopping rule that balances short- and long-term connections. (2) Shared path extraction module identifies both shared-head paths and shared-tail paths. (3) Time-decayed path encoding module processes these paths differently. (4) Gated information aggregation module combines path information to determine which parts matter most. (5) Attention decoding module makes the final prediction by focusing on the most relevant path features. Experiments on multiple TKG benchmark datasets demonstrate that RME outperforms the state-of-the-art methods in relation multi-path reasoning.

Current large vision-language models (VLMs) exhibit remarkable performance in basic video understanding tasks. However, existing VLMs are still limited to surface-level perception and lack fine-grained spatio-temporal understanding and combinatorial reasoning capabilities. Existing methods typically rely on expensive human annotations or subtitle extraction, yet they struggle to effectively model temporal relations between frames. This paper proposes a structured representation based on temporal triplets to address two major challenges in traditional approaches: temporal fragmentation and entity reference ambiguity. By modeling objects, attributes, and relationships within the video and incorporating temporal information, we convert semantic content from keyframes into a sequence of temporal triplets. This structured representation is then used as input for zero-shot video question answering (VideoQA). Experiments were conducted on four benchmark VideoQA datasets: NExT-QA, STAR, MSVD-QA, and MSRVTT-QA, showing that our method achieves competitive performance without requiring fine-tuning, validating its generality and effectiveness.

Multi-label intent detection aims to recognize multiple user intents behind dialogue utterances. The diversity of user utterances and the scarcity of training data motivate multi-label few-shot intent detection. However, existing methods ignore the hybrid of verb and noun within an intent, which is essential to identify the user intent. In this paper, we propose a label hierarchical structure-aware method for multi-label few-shot intent detection via prompt tuning (LHS). Firstly, for the support data, we concatenate the original utterance with the label description generated by GPT-4 to obtain the utterance-level representation. Then we construct a multi-label hierarchical structure-aware prompt model to learn the label hierarchical information. To learn more discriminative class prototypes, we devise a prototypical contrastive learning method to pull the utterances close to their corresponding intent labels and away from other intent labels. Extensive experiments on two datasets demonstrate the superiority of our method.

Meta-learning methods have shown to be effective in dealing with cold-start recommendation. However, most previous methods rely on an ideal assumption that there exists a similar data distribution between source and target tasks, which are unsuitable for the scenario that only extremely limited number of new user or item interactions are available. In this paper, we propose to boost meta-learning cold-start recommendation with graph neural network (MeGNN). First, it utilizes the global neighborhood translation learning to obtain consistent potential interactions for all new user and item nodes, which can refine their representations. Second, it employs the local neighborhood translation learning to predict specific potential interactions for each node, thus guaranteeing the personalized requirement. In experiments, we combine MeGNN with two representative meta-learning models MeLU and TaNP. Extensive results on two widely-used datasets show the superiority of MeGNN in four different scenarios.

Attributed graph clustering is a challenging task as it requires to jointly model graph structure and node attributes. Although recent advances in graph convolutional networks have shown the effectiveness of graph convolution in combining structural and content information, there is limited understanding of how to properly apply it for attributed graph clustering. Previous methods commonly use a fixed and low order graph convolution, which only aggregates information of few-hop neighbours and hence cannot fully capture the cluster structures of diverse graphs. In this paper, we first propose an adaptive graph convolution method (AGC) for attributed graph clustering, which exploits high-order graph convolutions to capture global cluster structures and adaptively selects an appropriate order$k$via intra-cluster distance. While AGC can find a reasonable$k$and avoid over-smoothing, it is not sensitive to the gradual decline of clustering performance as$k$increases. To search for a better$k$, we further propose an improved adaptive graph convolution method (IAGC) that not only observes the variation of intra-cluster distance, but also considers the inconsistencies of filtered features with graph structure and raw features, respectively. We establish the validity of our methods by theoretical analysis and extensive experiments on various benchmark datasets.

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention. As annotating large amounts of data is time-consuming and labor-intensive, data scarcity occurs frequently in real-world scenarios, which motivates multi-label few-shot aspect category detection. However, research on this problem is still in infancy and few methods are available. In this paper, we propose a novel label-enhanced prototypical network (LPN) for multi-label few-shot aspect category detection. The highlights of LPN can be summarized as follows. First, it leverages label description as auxiliary knowledge to learn more discriminative prototypes, which can retain aspect-relevant information while eliminating the harmful effect caused by irrelevant aspects. Second, it integrates with contrastive learning, which encourages that the sentences with the same aspect label are pulled together in embedding space while simultaneously pushing apart the sentences with different aspect labels. In addition, it introduces an adaptive multi-label inference module to predict the aspect count in the sentence, which is simple yet effective. Extensive experimental results on three datasets demonstrate that our proposed model LPN can consistently achieve state-of-the-art performance.

Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data. To obtain more satisfactory performance, the crucial points lie in two aspects: extracting better utterance features and strengthening the model generalization ability. In this paper, we propose a simple yet effective meta-learning paradigm for zero-shot intent classification. To learn better semantic representations for utterances, we introduce a new mixture attention mechanism, which encodes the pertinent word occurrence patterns by leveraging the distributional signature attention and multi-layer perceptron attention simultaneously. To strengthen the transfer ability of the model from seen classes to unseen classes, we reformulate zero-shot intent classification with a meta-learning strategy, which trains the model by simulating multiple zero-shot classification tasks on seen categories, and promotes the model generalization ability with a meta-adapting procedure on mimic unseen categories. Extensive experiments on two real-world dialogue datasets in different languages show that our model outperforms other strong baselines on both standard and generalized zero-shot intent classification tasks.

Deep cross-modal retrieval methods have shown their competitiveness among different cross-modal retrieval algorithms. Generally, these methods require a large amount of training data. However, aggregating large amounts of data will incur huge privacy risks and high maintenance costs. Inspired by the recent success of federated learning, we propose the federated cross-modal retrieval (FedCMR), which learns the model with decentralized multi-modal data. Specifically, we first train the cross-modal retrieval model and learn the common space across multiple modalities in each client using its local data. Then, we jointly learn the common subspace of multiple clients on the trusted central server. Finally, each client updates the common subspace of the local model based on the aggregated common subspace on the server, so that all clients participated in the training can benefit from federated learning. Experiment results on four benchmark datasets demonstrate the effectiveness proposed method.

Deep multimodal clustering have shown their competitiveness among different multimodal clustering algorithms. Existing algorithms usually boost the multimodal clustering by exploring the common knowledge among multiple modalities, which underutilizes the uniqueness of multiple modalities. In this paper, we enhance the mining of modality-common knowledge by extracting the modality-unique knowledge of each modality simultaneously. Specifically, we first utilize autoencoders to extract the modality-common and modality-unique features of each modality respectively. Meanwhile, the cross reconstruction is used to build latent connections among different modalities, i.e., maintain the consistency of modality-common features of each modality as well as heightening the diversity of modality-unique features of each modality. After that, modality-common features are fused to cluster the multimodal data. Experimental results on several benchmark datasets demonstrate that the proposed method outperforms state-of-art works obviously.

Multi-task clustering improves the clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task clustering methods are based on the ideal assumption that the tasks are completely related. However, in real applications, the tasks are usually partially related. In these cases, brute-force transfer may cause negative effect which degrades the clustering performance. In this paper, we propose two multi-task clustering methods for partially related tasks: the self-adapted multi-task clustering (SAMTC) method and the manifold regularized coding multi-task clustering (MRCMTC) method, which can automatically identify and transfer related instances among the tasks, thus avoiding negative transfer. Both SAMTC and MRCMTC construct the similarity matrix for each target task by exploiting useful information from the source tasks through related instances transfer, and adopt spectral clustering to get the final clustering results. But, they learn the related instances from the source tasks in different ways. Experimental results on real data sets show the superiorities of the proposed algorithms over traditional single-task clustering methods and existing multi-task clustering methods on both completely and partially related tasks.

Extracting fine-grained relations between entities of interest is of great importance to information extraction and large-scale knowledge graph construction. Conventional approaches on relation extraction require an existing knowledge graph to start with or sufficient observed samples from each relation type in the training process. However, such resources are not always available, and fine-grained manual labeling is extremely time-consuming and requires extensive expertise for specific domains such as healthcare and bioinformatics. Additionally, the distribution of fine-grained relations is often highly imbalanced in practice. We tackle this label scarcity and distribution imbalance issue from a one-shot classification perspective via a convolutional siamese neural network which extracts discriminative semantic-aware features to verify the relations between a pair of input samples. The proposed siamese network effectively extracts uncommon relations with only limited observed samples on the tasks of 1-shot and few-shot classification, demonstrating significant benefits to domain-specific information extraction in practical applications.

Many kinds of real world data can be modeled by a heterogeneous information network (HIN) which consists of multiple types of objects. Clustering plays an important role in mining knowledge from HIN. Several HIN clustering algorithms have been proposed in recent years. However, these algorithms suffer from one or moreof the following problems: (1) inability to model general HINs, (2) inability to simultaneously generate clusters for all types of objects, (3) inability to use similarity information of the objects with the same type. In this paper, we propose a powerful HIN clustering algorithm which can handle general HINs, simultaneously generate clusters for all types of objects, and use the similarity information of the same type of objects. First, we transform a general HIN into a meta-path-encoded relationship set. Second, we propose a nonnegative matrix tri-factorization multi-type co-clustering method, HMFClus, to cluster all types of objects in HIN simultaneously. Third, we integrate the information between the objects with the same type into HMFClus by using a similarity regularization. Extensive experiments on real world datasets show that the proposed algorithm outperforms the state-of-the-art methods.

Sampling is the key aspect for Nyström extension based spectral clustering. Traditional sampling schemes select the set of landmark points on a whole and focus on how to lower the matrix approximation error. However, the matrix approximation error does not have direct impact on the clustering performance. In this article, we propose a sampling framework from an incremental perspective, i.e., the landmark points are selected one by one, and each next point to be sampled is determined by previously selected landmark points. Incremental sampling builds explicit relationships among landmark points; thus, they work together well and provide a theoretical guarantee on the clustering performance. We provide two novel analysis methods and propose two schemes for selecting-the-next-one of the framework. The first scheme is based on clusterability analysis, which provides a better guarantee on clustering performance than schemes based on matrix approximation error analysis. The second scheme is based on loss analysis, which provides maximized predictive ability of the landmark points on the (implicit) labels of the unsampled points. Experimental results on a wide range of benchmark datasets demonstrate the superiorities of our proposed incremental sampling schemes over existing sampling schemes.

Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, we introduce a multi-task multi-view clustering framework which integrates within-view-task clustering, multi-view relationship learning, and multi-task relationship learning. Under this framework, we propose two multi-task multi-view clustering algorithms, the bipartite graph based multi-task multi-view clustering algorithm, and the semi-nonnegative matrix tri-factorization based multi-task multi-view clustering algorithm. The former one can deal with the multi-task multi-view clustering of nonnegative data, the latter one is a general multi-task multi-view clustering method, i.e., it can deal with the data with negative feature values. Experimental results on publicly available data sets in web page mining and image mining show the superiority of the proposed multi-task multi-view clustering algorithms over either multi-task clustering algorithms or multi-view clustering algorithms for multi-task clustering of multi-view data.

Twitter has become a target platform for both promoters and spammers to disseminate their messages, which are more harmful than traditional spamming methods, such as email spamming. Recently, large amounts of campaigns that contain lots of spam or promotion accounts have emerged in Twitter. The campaigns cooperatively post unwanted information, and thus they can infect more normal users than individual spam or promotion accounts. Organizing or participating in campaigns has become the main technique to spread spam or promotion information in Twitter. Since traditional solutions focus on checking individual accounts or messages, efficient techniques for detecting spam and promotion campaigns in Twitter are urgently needed. In this article, we propose a framework to detect both spam and promotion campaigns. Our framework consists of three steps: the first step links accounts who post URLs for similar purposes; the second step extracts candidate campaigns that may be for spam or promotion purposes; and the third step classifies the candidate campaigns into normal, spam, and promotion groups. The key point of the framework is how to measure the similarity between accounts' purposes of posting URLs. We present two measure methods based on Shannon information theory: the first one uses the URLs posted by the users, and the second one considers both URLs and timestamps. Experimental results demonstrate that the proposed methods can extract the majority of the candidate campaigns correctly, and detect promotion and spam campaigns with high precision and recall.

Traditional clustering algorithms deal with a single clustering task on a single dataset. However, there are many related tasks in the real world, which motivates multitask clustering. Recently some multitask clustering algorithms have been proposed, and among them multitask Bregman clustering (MBC) is a very applicable method. MBC alternatively updates clusters and learns relationships between clusters of different tasks, and the two phases boost each other. However, the boosting does not always have positive effects on improving the clustering performance, it may also cause negative effects. Another issue of MBC is that it cannot deal with nonlinear separable data. In this article, we show that in MBC, the process of using cluster relationship to boost the cluster updating phase may cause negative effects, that is, cluster centroids may be skewed under some conditions. We propose a smart multitask Bregman clustering (S-MBC) algorithm which can identify the negative effects of the boosting and avoid the negative effects if they occur. We then propose a multitask kernel clustering (MKC) framework for nonlinear separable data by using a similar framework like MBC in the kernel space. We also propose a specific optimization method, which is quite different from that of MBC, to implement the MKC framework. Since MKC can also cause negative effects like MBC, we further extend the framework of MKC to a smart multitask kernel clustering (S-MKC) framework in a similar way that S-MBC is extended from MBC. We conduct experiments on 10 real world multitask clustering datasets to evaluate the performance of S-MBC and S-MKC. The results on clustering accuracy show that: (1) compared with the original MBC algorithm MBC, S-MBC and S-MKC perform much better; (2) compared with the convex discriminative multitask relationship clustering (DMTRC) algorithms DMTRC-L and DMTRC-R which also avoid negative transfer, S-MBC and S-MKC perform worse in the (ideal) case in which different tasks have the same cluster number and the empirical label marginal distribution in each task distributes evenly, but better or comparable in other (more general) cases. Moreover, S-MBC and S-MKC can work on the datasets in which different tasks have different number of clusters, violating the assumptions of DMTRC-L and DMTRC-R. The results on efficiency show that S-MBC and S-MKC consume more computational time than MBC and less computational time than DMTRC-L and DMTRC-R. Overall S-MBC and S-MKC are competitive compared with the state-of-the-art multitask clustering algorithms in synthetical terms of accuracy, efficiency and applicability.

Multi-view clustering integrates complementary information from multiple views to gain better clustering performance rather than relying on a single view. NMF based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, NMF fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized nonnegative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF regards that the intrinsic manifold of the dataset is embedded in a convex hull of all the views' manifolds, and incorporates such an intrinsic manifold and an intrinsic (consistent) coefficient matrix with a multi-manifold regularizer to preserve the locally geometrical structure of the multi-view data space. We use linear combination to construct the intrinsic manifold, and propose two strategies to find the intrinsic coefficient matrix, which lead to two instances of the framework. Experimental results show that the proposed algorithms outperform existing NMF based algorithms for multi-view clustering.

Semi-automatic anti-spam algorithms propagate either trust through links from a good seed set (e.g., TrustRank) or distrust through inverse links from a bad seed set (e.g., Anti-TrustRank) to the entire Web. These kinds of algorithms have shown their powers in combating link-based Web spam since they integrate both human judgement and machine intelligence. Nevertheless, there is still much space for improvement. One issue of most existing trust/distust propagation algorithms is that only trust or distrust is propagated and only a good seed set or a bad seed set is used. According to Wu et al. [2006a], a combined usage of both trust and distrust propagation can lead to better results, and an effective framework is needed to realize this insight. Another more serious issue of existing algorithms is that trust or distrust is propagated in nondifferential ways, that is, a page propagates its trust or distrust score uniformly to its neighbors, without considering whether each neighbor should be trusted or distrusted. Such kinds of blind propagating schemes are inconsistent with the original intention of trust/distrust propagation. However, it seems impossible to implement differential propagation if only trust or distrust is propagated. In this article, we take the view that each Web page has both a trustworthy side and an untrustworthy side, and we thusly assign two scores to each Web page: T-Rank, scoring the trustworthiness of the page, and D-Rank, scoring the untrustworthiness of the page. We then propose an integrated framework that propagates both trust and distrust. In the framework, the propagation of T-Rank/D-Rank is penalized by the target's current D-Rank/T-Rank. In other words, the propagation of T-Rank/D-Rank is decided by the target's current (generalized) probability of being trustworthy/untrustworthy; thus a page propagates more trust/distrust to a trustworthy/untrustworthy neighbor than to an untrustworthy/trustworthy neighbor. In this way, propagating both trust and distrust with target differentiation is implemented. We use T-Rank scores to realize spam demotion and D-Rank scores to accomplish spam detection. The proposed Trust-DistrustRank (TDR) algorithm regresses to TrustRank and Anti-TrustRank when the penalty factor is set to 1 and 0, respectively. Thus TDR could be seen as a combinatorial generalization of both TrustRank and Anti-TrustRank. TDR not only makes full use of both trust and distrust propagation, but also overcomes the disadvantages of both TrustRank and Anti-TrustRank. Experimental results on benchmark datasets show that TDR outperforms other semi-automatic anti-spam algorithms for both spam demotion and spam detection tasks under various criteria.

The Twitter social network has become a target platform for both promoters and stammers to disseminate their target messages. There are a large number of campaigns containing coordinated spam or promoting accounts in Twitter, which are more harmful than the traditional methods, such as email spamming. Since traditional solutions mainly check individual accounts or messages, it is an urgent task to detect spam and promoting campaigns in Twitter. In this paper, we propose a scalable framework to detect both spam and promoting campaigns. Our framework consists of three steps: firstly linking accounts who post URLs for similar purposes, secondly extracting candidate campaigns which may exist for spam or promoting purpose and finally distinguishing their intents. One salient aspect of the framework is introducing a URL-driven estimation method to measure the similarity between accounts' purposes of posting URLs, the other one is proposing multiple features to distinguish the candidate campaigns based on a machine learning method. Over a large-scale dataset from Twitter, we can extract the actual campaigns with high precision and recall and distinguish the majority of the candidate campaigns correctly.

To alleviate the memory and computational burdens of spectral clustering for large scale problems, some kind of low-rank matrix approximation is usually employed. Nyström method is an efficient technique to generate low rank matrix approximation and its most important aspect is sampling. The matrix approximation errors of several sampling schemes have been theoretically analyzed for a number of learning tasks. However, the impact of matrix approximation error on the clustering performance of spectral clustering has not been studied. In this paper, we firstly analyze the performance of Nyström method in terms of cluster ability, thus answer the impact of matrix approximation error on the clustering performance of spectral clustering. Our analysis immediately suggests an incremental sampling scheme for the Nyström method based spectral clustering. Experimental results show that the proposed incremental sampling scheme outperforms existing sampling schemes on various clustering tasks and image segmentation applications, and its efficiency is comparable with existing sampling schemes.

Graph-based semi-supervised learning algorithms have attracted a lot of attention. Constructing a good graph is playing an essential role for all these algorithms. Many existing graph construction methods(e.g. Gaussian Kernel etc.) require user input parameter, which is hard to configure manually. In this paper, we propose a parameter-free similarity measure Adaptive Similarity Estimation (ASE), which constructs the graph by adaptively optimizing linear combination of its neighbors. Experimental results show the effectiveness of our proposed method.

Clusters are hidden in subspaces of high dimensional data, i.e., only a subset of features is relevant for each cluster. Subspace clustering is challenging since the search for the relevant features of each cluster and the detection of the final clusters are circular dependent and should be solved simultaneously. In this paper, we point out that feature correlation and distance divergence are important to subspace clustering, but both have not been considered in previous works. Feature correlation groups correlated features independently thus helps to reduce the search space for the relevant features search problem. Distance divergence distinguishes distances on different dimensions and helps to find the final clusters accurately. We tackle the two problems with the aid of a small amount domain knowledge in the form of must-links and cannot-links. We then devise a semi-supervised subspace clustering algorithm CDCDD. CDCDD integrates our solutions of the feature correlation and distance divergence problems, and uses an adaptive dimension voting scheme, which is derived from a previous unsupervised subspace clustering algorithm FINDIT. Experimental results on both synthetic data sets and real data sets show that the proposed CDCDD algorithm outperforms FINDIT in terms of accuracy, and outperforms the other constraint based algorithm SCMINER in terms of both accuracy and efficiency.