Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this long-standing challenge, we propose a novel and fully parameter-free clustering framework via Self-supervised Consensus Maximization, named SCMax. Our framework performs hierarchical agglomerative clustering and cluster evaluation in a single, integrated process. At each step of agglomeration, it creates a new, structure-aware data representation through a self-supervised learning task guided by the current clustering structure. We then introduce a nearest neighbor consensus score, which measures the agreement between the nearest neighbor-based merge decisions suggested by the original representation and the self-supervised one. The moment at which consensus maximization occurs can serve as a criterion for determining the optimal number of clusters. Extensive experiments on multiple datasets demonstrate that the proposed framework outperforms existing clustering approaches designed for scenarios with an unknown number of clusters.

Multi-view clustering (MVC) leverages complementary information across heterogeneous views to improve unsupervised partitioning. Nevertheless, the majority of existing MVC methods critically assume that samples are fully aligned across views, an assumption frequently violated in practice when multi-view data are collected from independent sources without any correspondence. This gives rise to Completely Unaligned multi-view Clustering (CUC), where cross-view sample correspondences are entirely unknown, fundamentally impeding effective multi-view fusion. Prior CUC-oriented methods typically infer inter-view relations from distance/similarity matrices; however, severe cross-view heterogeneity often induces over-smoothing in such matrices, leading to unreliable matching signals and degraded clustering performance. To address these issues, we propose Cross-view Graph Matching for Completely Unaligned multi-view Clustering (CGM-CUC), a unified framework that couples structure-aware representation learning with progressive cross-view alignment. Specifically, CGM-CUC introduces a bipartite graph-based sample re-encoding mechanism to enhance discriminative structural cues, and an iterative cross-view matching network that progressively refines permutation matrices to recover latent correspondences. Moreover, we develop an alignment-guided optimization strategy that mitigates the over-smoothing effect in similarity estimation, thereby stabilizing the matching process and improving downstream clustering. Extensive experiments on multiple benchmark datasets demonstrate that CGM-CUC consistently achieves superior performance over state-of-the-art baselines, with particularly notable gains under fully unaligned view settings.

Anchor selection or learning has become a critical component in large-scale multi-view clustering. Existing anchor-based methods, which either select-then-fix or initialize-then-optimize with orthogonality, yield promising performance. However, these methods still suffer from instability of initialization or insufficient depiction of data distribution. Moreover, the desired properties of anchors in multi-view clustering remain unspecified. To address these issues, this paper first formalizes the desired characteristics of anchors, namely Diversity, Balance and Compactness. We then devise and mathematically validate anchors that satisfy these properties by maximizing the Mahalanobis distance between anchors. Furthermore, we introduce a novel method called Max-Mahalanobis Anchors Guidance for multi-view Clustering (MAGIC), which guides the cross-view representations to progressively align with our well-defined anchors. This process yields highly discriminative and compact representations, significantly enhancing the performance of multi-view clustering. Experimental results show that our meticulously designed strategy significantly outperforms existing anchor-based methods in enhancing anchor efficacy, leading to substantial improvement in multi-view clustering performance.

Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\textbf{all}$ views. To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work. It firstly transforms partial bipartition learning into original sample form by virtue of reconstruction concept to split out of observed similarity, and then loosens traditional non-negative constraints via regularizing samples to more freely characterize the similarity. Subsequently, it learns to recover the incomplete parts by utilizing the connection built between the similarity exclusive on respective view and the consensus graph shared for all views. On this foundation, it further introduces a group of hybrid prototype quantities for each individual view to flexibly extract the data features belonging to each view itself. Accordingly, the resulting graphs are with various scales and describe the overall similarity more comprehensively. It is worth mentioning that these all are optimized in one unified learning framework, which makes it possible for them to reciprocally promote. Then, to effectively solve the formulated optimization problem, we design an ingenious auxiliary function that is with theoretically proven monotonic-increasing properties. Finally, the clustering results are obtained by implementing spectral grouping action on the eigenvectors of stacked multi-scale consensus similarity. Experimental results confirm the effectiveness of SIIHPC.

Recent advancements in multiple kernel clustering (MKC) have highlighted the effectiveness of late fusion strategies, particularly in enhancing computational efficiency to near-linear complexity while achieving promising clustering performance. However, existing methods encounter three significant limitations: (1) reliance on fixed base partition matrices that do not adaptively optimize during the clustering process, thereby constraining their performance to the inherent representational capabilities of these matrices; (2) a focus on adjusting kernel weights to explore inter-view consistency and complementarity, which often neglects the intrinsic high-order correlations among views, thereby limiting the extraction of comprehensive multiple kernel information; (3) a lack of adaptive mechanisms to accommodate varying distributions within the data, which limits robustness and generalization. To address these challenges, this paper proposes a novel algorithm termed Dynamic Late Fusion Multiple Kernel Clustering with Robust {Tensor Learning via min-max optimization (DLEFT-MKC), which effectively overcomes the representational bottleneck of base partition matrices and facilitates the learning of meaningful high-order cross-view information. Specifically, it is the first to incorporate a min-max optimization paradigm into tensor-based MKC, enhancing algorithm robustness and generalization. Additionally, it dynamically reconstructs decision layers to enhance representation capabilities and subsequently stacks the reconstructed representations for tensor learning that promotes the capture of high-order associations and cluster structures across views, ultimately yielding consensus clustering partitions. To solve the resultant optimization problem, we innovatively design a strategy that combines reduced gradient descent with the alternating direction method of multipliers, ensuring convergence to local optima while maintaining high computational efficiency. Extensive experimental results across various benchmark datasets validate the superior effectiveness and efficiency of the proposed DLEFT-MKC.

Multi-view clustering (MVC) leverages complementary information from diverse data sources to enhance clustering performance. However, its practical deployment in distributed and privacy-sensitive scenarios remains challenging. Federated multi-view clustering (FMVC) has emerged as a potential solution, but existing approaches suffer from substantial limitations, including excessive communication overhead, insufficient privacy protection, and inadequate handling of missing views. To address these issues, we propose Efficient Federated Incomplete Multi-View Clustering (EFIMVC), a novel framework that introduces a localized optimization strategy to significantly reduce communication costs while ensuring theoretical convergence. EFIMVC employs both view-specific and shared anchor graphs as communication variables, thereby enhancing privacy by avoiding the transmission of sensitive embeddings. Moreover, EFIMVC seamlessly extends to scenarios with missing views, making it a practical and scalable solution for real-world applications. Extensive experiments on benchmark datasets demonstrate the superiority of EFIMVC in clustering accuracy, communication efficiency, and privacy preservation. Our code is publicly available at https://github.com/Tracesource/EFIMVC.

Despite remarkable advances, existing incomplete multi-view clustering (IMC) methods typically leverage either perspective-shared or perspective-specific determinants to encode cluster representations. To address this limitation, we introduce a BACDL algorithm designed to explicitly capture both concurrently, thereby exploiting heterogeneous data more effectively. It chooses to bifurcate feature clusters and further alienate them to enlarge the discrimination. With distribution learning, it successfully couples view guidance into feature clusters to alleviate dimension inconsistency. Then, building on the principle that samples in one common cluster own similar marginal distribution and conditional distribution, it unifies the association between feature clusters and sample clusters to bridge all views. Thereafter, all incomplete sample clusters are reordered and mapped to a common one to formulate clustering embedding. Last, the overall linear overhead endows it with a resource-efficient characteristic.

Current multi-view clustering (MVC) techniques generally focus only on the relationship between anchors and samples, while overlooking that between anchors. Moreover, due to the lack of data labels, the cluster order is inconsistent across views and accordingly anchors encounter misalignment, which will confuse the graph structure and disorganize cluster representation. Even worse, it typically brings variance during forming spectral embedding, degenerating the stability of clustering results. In response to these concerns, in the paper we propose a MVC approach named DTP-SF-BVF. Concretely, we explicitly exploit the geometric properties between anchors via self-expression learning skill, and utilize topology learning strategy to feed captured anchor-anchor features into anchor-sample graph so as to explore the manifold structure hidden within samples more adequately. To reduce the misalignment risk, we introduce a permutation mechanism for each view to jointly rearrange anchors according to respective view characteristics. Besides not involving selecting the baseline view, it also can coordinate with anchors in the unified framework and thereby facilitate the learning of anchors. Further, rather than forming spectrum and then performing embedding partitioning, based on the criterion that samples and clusters should be hard assignment, we manage to construct the cluster labels directly from original samples using the binary strategy, not only preserving the data diversity but avoiding variance. Experiments on multiple publicly available datasets confirm the effectiveness of proposed DTP-SF-BVF method.

In multi-view clustering (MVC), anchor technique is generally hailed as an effective means for filtering noise and improving computation efficiency. However, existing methods usually construct anchors via heuristic strategy, random sampling, or orthogonal learning, which overlook the distribution differences between anchors and original data, leading to anchors lacking structural characteristics. To generate the anchors that are with similar distributions to original data, in the paper we carefully devise a LASD algorithm from the perspective of optimal transport (OT). Concretely, we firstly design a Multi-View OT (MVOT) framework through complementary and consensus representation learning. Then, we theoretically demonstrate the convexity of MVOT using the positive semidefiniteness of its Hessian matrix, and accordingly the global optimal solution of each transport plan can be reached. Further, we establish the strong dual condition for MVOT by the relative interior. Based on dual programming, consequently, we successfully obtain the transport plan between anchors and original data for each view within linear computational complexity. Afterwards, the spectral clustering operation is employed on the consensus plan to produce the discrete cluster labels. Abundant experiments underscore that our learned anchors do well reflect the distributions of original data, and the generated clustering results outperform multiple strong MVC competitors, even under large-scale scenarios. The source code is available at https://github.com/junpuzhang/LASD.

Although receiving notable improvements, current multi-view clustering (MVC) techniques generally rely on feature library mechanisms to propagate accumulated knowledge from historical views to newly-arrived data, which overlooks the information pertaining to basis embedding within each view. Moreover, the mapping paradigm inevitably alters the values of learned landmarks and built affinities due to the uninterruption nature, accordingly disarraying the hierarchical cluster structures. To mitigate these two issues, we in the paper provide a named BSTM algorithm. Concretely, we firstly synchronize with the distinct dimensions by introducing a group of specialized projectors, and then establish unified anchors for all views collected so far to capture intrinsic patterns. Afterwards, departing from per-view architectures, we devise a shared bipartite graph construction via indicators to quantify similarity, which not only avoids redundant data-recalculations but alleviates the representation distortion caused by fusion. Crucially, there two components are optimized within an integrated framework, and collectively facilitate knowledge transfer upon encountering incoming views. Subsequently, to flexibly do transformation on anchors and meanwhile maintain numerical consistency, we develop a bit-swapping scheme operating exclusively on 0 and 1. It harmonizes anchors on current view and that on previous views through one-hot encoded row and column attributes, and the graph structures are correspondingly reordered to reach a matched configuration. Furthermore, a computationally efficient four-step updating strategy with linear complexity is designed to minimize the associated loss. Extensive experiments organized on publicly-available benchmark datasets with varying missing percentages confirm the superior effectiveness of our BSTM.

Multi-view fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC researches. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: (1) multiple views within a single client; (2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives.

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due to the sampling operation being performed on each individual view independently and not considering the distribution of samples in all views, the produced anchors are usually slightly distinguishable, failing to characterize the whole data. Moreover, it is necessary to fuse multiple separated graphs into one, which leads to the final clustering performance heavily subject to the fusion algorithm adopted. What is worse, existing MVSC methods generate dense bipartite graphs, where each sample is associated with all anchor candidates. We argue that this dense-connected mechanism will fail to capture the essential local structures and degrade the discrimination of samples belonging to the respective near anchor clusters. To alleviate these issues, we devise a clustering framework named SL-CAUBG. Specifically, we do not utilize sampling strategy but optimize to generate the consensus anchors within all views so as to explore the information between different views. Based on the consensus anchors, we skip the fusion stage and directly construct the unified bipartite graph across views. Most importantly, norm and Laplacian-rank constraints employed on the unified bipartite graph make it capture both local and global structures simultaneously. norm helps eliminate the scatters between anchors and samples by constructing sparse links and guarantees our graph to be with clear anchor-sample affinity relationship. Laplacian-rank helps extract the global characteristics by measuring the connectivity of unified bipartite graph. To deal with the nondifferentiable objective function caused by norm, we adopt an iterative re-weighted method and the Newton's method. To handle the nonconvex Laplacian-rank, we equivalently transform it as a convex trace constraint. We also devise a four-step alternate method with linear complexity to solve the resultant problem. Substantial experiments show the superiority of our SL-CAUBG.

In numerous real-world applications, it is quite common that sample information is partially available for some views due to machine breakdown or sensor failure, causing the problem of incomplete multi-view clustering (IMVC). While several IMVC approaches using view-shared anchors have successfully achieved pleasing performance improvement, (1) they generally construct anchors with only one dimension, which could deteriorate the multi-view diversity, bringing about serious information loss; (2) the constructed anchors are typically with a single size, which could not sufficiently characterize the distribution of the whole samples, leading to limited clustering performance. For generating view-shared anchors with multi-dimension and multi-size for IMVC, we design a novel framework called Diverse View-Shared Anchors based Incomplete multi-view clustering (DVSAI). Concretely, we associate each partial view with several potential spaces. In each space, we enable anchors to communicate among views and generate the view-shared anchors with space-specific dimension and size. Consequently, spaces with various scales make the generated view-shared anchors enjoy diverse dimensions and sizes. Subsequently, we devise an integration scheme with linear computational and memory expenditures to integrate the outputted multi-scale unified anchor graphs such that running spectral algorithm generates the spectral embedding. Afterwards, we theoretically demonstrate that DVSAI owns linear time and space costs, thus well-suited for tackling large-size datasets. Finally, comprehensive experiments confirm the effectiveness and advantages of DVSAI.

Multi-view graph clustering (MVGC) derives encouraging grouping results by seamlessly integrating abundant information inside heterogeneous data, and has captured surging focus recently. Nevertheless, the majority of current MVGC works involve at least one hyper-parameter, which not only requires additional efforts for tuning, but also leads to a complicated solving procedure, largely harming the flexibility and scalability of corresponding algorithms. To this end, in the article we are devoted to getting rid of hyper-parameters, and devise a non-parametric graph clustering (NpGC) framework to more practically partition multi-view data. To be specific, we hold that hyper-parameters play a role in balancing error item and regularization item so as to form high-quality clustering representations. Therefore, under without the assistance of hyper-parameters, how to acquire high-quality representations becomes the key. Inspired by this, we adopt two types of anchors, view-related and view-unrelated, to concurrently mine exclusive characteristics and common characteristics among views. Then, all anchors' information is gathered together via a consensus bipartite graph. By such ways, NpGC extracts both complementary and consistent multi-view features, thereby obtaining superior clustering results. Also, linear complexities enable it to handle datasets with over 120000 samples. Numerous experiments reveal NpGC's strong points compared to lots of classical approaches.

In this paper, we derive an upper bound of the difference between a kernel matrix and its expectation under a mild assumption. Specifically, we assume that the true distribution of the training data is an unknown isotropic Gaussian distribution. When the kernel function is a Gaussian kernel, and the mean of each cluster is sufficiently separated, we find that the expectation of a kernel matrix can be close to a rank-$k$ matrix, where $k$ is the cluster number. Moreover, we prove that the normalized kernel matrix of the training set deviates (w.r.t. Frobenius norm) from its expectation in the order of $\widetilde{\mathcal{O}}(1/\sqrt{d})$, where $d$ is the dimension of samples. Based on the above theoretical results, we propose a novel multiple kernel clustering framework which attempts to learn the information of the expectation kernel matrices. First, we aim to minimize the distance between each base kernel and a rank-$k$ matrix, which is a proxy of the expectation kernel. Then, we fuse these rank-$k$ matrices into a consensus rank-$k$ matrix to find the clustering structure. Using an anchor-based method, the proposed framework is flexible with the sizes of input kernel matrices and able to handle large-scale datasets. We also provide the approximation guarantee by deriving two non-asymptotic bounds for the consensus kernel and clustering indicator matrices. Finally, we conduct extensive experiments to verify the clustering performance of the proposed method and the correctness of the proposed theoretical results.

Sample labeling is the most primary and fundamental step of semi-supervised learning. In literature, most existing methods randomly label samples with a given ratio, but achieve unpromising and unstable results due to the randomness, especially in multi-view settings. To address this issue, we propose a Dynamic Multi-view Labeling Strategy with Shared and Specific Information. To be brief, by building two classifiers with existing labels to utilize decoupled shared and specific information, we select the samples of low classification confidence and label them in high priorities. The newly generated labels are also integrated to update the classifiers adaptively. The two processes are executed alternatively until a satisfying classification performance. To validate the effectiveness of the proposed method, we conduct extensive experiments on popular benchmarks, achieving promising performance. The code is publicly available at https://github.com/wanxinhang/ICML2024_decouple_then_classify.

Incomplete multi-view clustering (IMVC) methods typically encounter three drawbacks: (1) intense time and/or space overheads; (2) intractable hyper-parameters; (3) non-zero variance results. With these concerns in mind, we give a simple yet effective IMVC scheme, termed as ToRES. Concretely, instead of self-expression affinity, we manage to construct prototype-sample affinity for incomplete data so as to decrease the memory requirements. To eliminate hyper-parameters, besides mining complementary features among views by view-wise prototypes, we also attempt to devise cross-view prototypes to capture consensus features for jointly forming high-quality clustering representation. To avoid the variance, we successfully unify representation learning and clustering operation, and directly optimize the discrete cluster indicators from incomplete data. Then, for the resulting objective function, we provide two equivalent solutions from perspectives of feasible region partitioning and objective transformation. Many results suggest that ToRES exhibits advantages against 20 SOTA algorithms, even in scenarios with a higher ratio of incomplete data.

Multi-View Clustering (MVC) commonly utilizes the anchor technique to mitigate the computational complexity. Existing methods generally assume a pre-selection of anchors to facilitate subsequent clustering tasks. However, the determination of the optimal number of anchors is often non-trivial and necessitates their treatment as a tunable parameter, incurring additional computational overhead. Moreover, it is not reasonable to assume an identical number of anchors across all views, as this assumption restricts the representational capacity of anchors in individual views. To address the above issues, we propose a view adaptive anchor multi-view clustering called Multi-view Clustering with Automatic and Aligned Anchor (3AMVC). We introduce a Hierarchical Bipartite Neighbor Clustering (HBNC) strategy to adaptively select a suitable number of representative anchors in each view. Specifically, when the representative difference of anchors lies in a acceptable and satisfactory range, the HBNC process is halted and picks out the final anchors. Moreover, we propose an innovative anchor alignment strategy in response to the varying quantities of anchors across different views. This approach initially evaluates the quality of anchors on each view based on the intra-cluster distance criterion and then proceeds to align based on the view with the highest-quality anchors. The carefully organized experiments well validate the effectiveness and strengthens of 3AMVC.

Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer from complex and cumbersome alternating optimization, limiting performance and scalability. To this end, we propose a novel intent learning method termed \underline{ELCRec}, by unifying behavior representation learning into an \underline{E}nd-to-end \underline{L}earnable \underline{C}lustering framework, for effective and efficient \underline{Rec}ommendation. Concretely, we encode user behavior sequences and initialize the cluster centers (latent intents) as learnable neurons. Then, we design a novel learnable clustering module to separate different cluster centers, thus decoupling users' complex intents. Meanwhile, it guides the network to learn intents from behaviors by forcing behavior embeddings close to cluster centers. This allows simultaneous optimization of recommendation and clustering via mini-batch data. Moreover, we propose intent-assisted contrastive learning by using cluster centers as self-supervision signals, further enhancing mutual promotion. Both experimental results and theoretical analyses demonstrate the superiority of ELCRec from six perspectives. Compared to the runner-up, ELCRec improves NDCG@5 by 8.9\% and reduces computational costs by 22.5\% on the Beauty dataset. Furthermore, due to the scalability and universal applicability, we deploy this method on the industrial recommendation system with 130 million page views and achieve promising results. The codes are available on GitHub\footnote{https://github.com/yueliu1999/ELCRec}. A collection (papers, codes, datasets) of deep group recommendation/intent learning methods is available on GitHub\footnote{https://github.com/yueliu1999/Awesome-Deep-Group-Recommendation}.

Multi-view subspace clustering (MVSC) is a popular area of research that concentrates on partitioning data points from multiple views. It has gained wide attention in recent years due to the ability to handle complex data with diverse features across different views. However, the success of MVSC largely relies on the quality of the learned similarity matrix, and existing methods normally adopt the separate two-step procedures of optimization and symmetrization, which could not guarantee symmetry and adaptive locality of the similarity matrix. To alleviate this issue, in this paper, we propose a novel paradigm called Symmetric Multi-view Subspace Clustering with Automatic Neighbor Discovery (SMSC-AND), which aims at formulating the symmetrization and localization of the ideal similarity matrix into one unified framework. In particular, we theoretically and experimentally demonstrate that SMSC-AND can directly receive the refined symmetric similarity matrix without previous post-processing procedures. Additionally, we propose an automatic neighbor discovery strategy that avoids previous rank constraints or fixed neighbor size, thereby eliminating the requirement for additional hyperparameters. Benefiting from the aforementioned merits, we can directly explore the local structure of the consensus similarity matrix of multi-view data without pre-searching hyperparameters. Comprehensive experimental results on various benchmark datasets have demonstrated the superiority of the proposed algorithm when compared with other MVSC competitors.

Anchor based incomplete multiview clustering has grasped growing interest recently because of its great success in effectively partitioning multimodal data. However, due to the absence of label information, the constructed anchors could be mismatched. Such an Anchor Mismatching Problem (AMP) will cause the structure of generated bipartite graph to be chaotic, degrading the clustering performance. To tackle this issue, we design an algorithm termed Constructing Corresponding Anchors for Incomplete Multiview Clustering (CCA-IMC). Specifically, we first devise a permutation strategy to transform anchors on each view. Subsequently, we directly generate the consensus bipartite graph, which is shared for all incomplete views, by the transformed anchors rather than by fusing each view-specific bipartite graph. Afterwards, all anchors and permutation matrices as well as the consensus bipartite graph are jointly optimized in one common framework so as to promote each other. In such ways, anchors are rearranged towards correct matching relationship according to the consensus graph structure. In addition to these, our CCA-IMC has also been proven to be with linear time and memory overheads, which makes it able to scale up to work with large-scale tasks. Massive experiments implemented on ten popular datasets give evidence of our superiorities compared to current strong IMC competitors.

Incomplete multiview clustering (IMVC) generally requires the number of anchors to be the same in all views. Also, this number needs to be tuned with extra manual efforts. This not only degenerates the diversity of multiview data but also limits the model's scalability. For generating differentiated numbers of anchors without tuning, in this article we devise a novel framework named DAQINT. To be specific, the most perfect solution is to jointly find the optimal number of anchors that belongs to respective view. Regretfully, it is extremely time consuming. In view of this, we choose to first offer a set of anchor numbers for each view, and then integrate their contributions by adaptive weighting to approximate the optimal number. In particular, these offered numbers are all predefined and do not require any tuning. Through adaptively weighting them, we hold that this equivalently makes each view enjoy a different number of anchors. Accordingly, the bipartite graphs generated on all views are with diverse scales. Besides exploring multiview features more deeply, they also balance the importance between views. Then, to fuse these multiscale bipartite graphs, we design a combination strategy that owns linear computation and storage overheads. Afterward, to solve the resulting optimization problem, we also carefully develop a three-step iterative algorithm with linear complexities and demonstrated convergence. Experiments on the multiple public datasets validate the superiority of DAQINT against several advanced IMVC methods, such as on Mfeat, DAQINT surpasses the competitors like MKC, EEIMVC, FLSD, DSIMVC, IMVC-CBG, and DCP by 36.65%, 6.33%, 48.53%, 22.46%, 15.06%, and 32.04%, respectively, in ACC.

Convolutional neural network has witnessed remarkable progress in compressed video quality enhancement in high efficiency video coding (HEVC) standard. But most existing methods focus on single frame quality enhancement where copious temporal and spatial information is neglected. In this paper, we propose a spatial-temporal fusion convolutional neural network (STEF-CNN) to employ spatial and temporal information to improve the performance of in-loop filter in HEVC. Specifically, the STEF-CNN adopts a pre-denoising network which in advance processes the compressed videos frame by frame. The pre-denoising operation alleviates the impact of noise and blocking artifacts. Then the denoised frames are sent to temporal-spatial fusion module which picks out valuable temporal and spatial information. The fused frames are eventually fed to quality enhancement network which is based on residual learning and dense network. The STEF-CNN is capable of capturing abundant information from consecutive neighboring frames. Extensive experimental results demonstrate the effectiveness of the proposed method. The STEF-CNN achieves 11.53% BD-BR reduction in all-intra (AI) configuration and 10.20% BD-BR reduction in random-access (RA) configuration.

Multi-view texture video coding is very important, we propose a bit allocation method based on view layer and a bitrate decision method for P-frame of the dependent view (DV). First of all, considering that the distortion in the base view (BV) is directly transmitted to the DV by inter-view skip mode, the RD model of the DV is improved based on the inter view dependency. In this paper, a precise power model is derived based on our joint RD model to represent the target bitrates relationship between the BV and the DV. Then, since the P frame in the DV (P-DV) is mainly predicted from the corresponding I frame in the BV (I-BV) by inter-view prediction, the constant proportional relationship between the P-DV and the I-BV is discovered in this paper. Based on this discovery, a novel linear model is built to assign the target bitrates of the P-DV. Extensive experimental results exhibit that the proposed scheme provides a better RD performance than the state-of-the-art algorithms.

In video encoder, hard-decision quantization (HDQ) is well-suited for parallel processing, but suffers from non-negligible coding performance degradation compared with soft-decision quantization (SDQ). In this paper, by fully simulating the behavior of SDQ, a coefficient-adaptive offset model constructed by the deep learning approach is proposed to adjust the output of HDQ. Experiment results show that the proposed algorithm achieves promising RD performance and well-suited for hardware encoder implementation design.

Image stitching technology has been widely used in immersive applications, such as 3D modeling, VR and AR. The quality of stitching results is crucial. At present, the objective quality assessment methods of stitched images are mainly based on the availability of ground truth (i.e., Full-Reference). However, in most cases, ground truth is unavailable. In this paper, a no-reference quality assessment metric specifically designed for stitched images is proposed. We first find out the corresponding parts of source images in the stitched image. Then, the isolated points and the outer points generated by spherical projection are eliminated. After that, we take advantage of the bounding rectangle of stitching seams to locate the position of overlapping regions in the stitched image. Finally, the assessment of overlapping regions is taken as the final scoring result. Extensive experiments have shown that our scores are consistent with human vision. Even for the nuances that cannot be distinguished by human eyes, our proposed metric is also effective.

The guidance of color images greatly improves the restoration accuracy of the depth map. However, due to the incomplete texture structure consistency between the color image and the depth map, and the limitations of$L^{2}$based model, static guidance tends to cause texture copy artifacts and blurring depth discontinuities, which seriously deteriorates the quality of result. To tackle those problems, we propose a dynamic guidance model which can adaptively adjustment itself based on local smooth characteristics and continuously optimize in iteration. The proposed method can significantly alleviate the negative impact of the incomplete consistency and makes full use of the guidance information to restore fine texture at the same time. Moreover, a novel edge correction mechanism is designed to filter out incorrect depth information around boundaries, ensuring the correction of edges. In experiments, visual comparison proves that our method can alleviate texture copy artifacts and blurring depth discontinuities effectively. Quantitative results show that the proposed method restores high quality depth map with the lowest mean absolute error.