Multi-view representation learning integrates multiple observable views of an entity into a unified representation to facilitate downstream tasks. Current methods predominantly focus on distinguishing compatible components across views, followed by a single-step parallel fusion process. However, this parallel fusion is static in essence, overlooking potential conflicts among views and compromising representation ability. To address this issue, this paper proposes a novel Sequential fusion framework for Multi-view Representation Learning, termed SeqMvRL. Specifically, we model multi-view fusion as a sequential decision-making problem and construct a pairwise integrator (PI) and a next-view selector (NVS), which represent the environment and agent in reinforcement learning, respectively. PI merges the current fused feature with the selected view, while NVS is introduced to determine which view to fuse subsequently. By adaptively selecting the next optimal view for fusion based on the current fusion state, SeqMvRL thereby effectively reduces conflicts and enhances unified representation quality. Additionally, an elaborate novel reward function encourages the model to prioritize views that enhance the discriminability of the fused features. Experimental results demonstrate that SeqMvRL outperforms parallel fusion schemes in classification and clustering tasks.

Meta-learning methods typically follow a two-loop framework, where each loop potentially suffers from notorious overfitting, hindering rapid adaptation and generalization to new tasks. Existing methods address this by enhancing the mutual-exclusivity or diversity of training samples, but these data manipulation strategies are data-dependent and insufficiently flexible. This work proposes a data-independent Meta-Gradient Augmentation (MGAug) method from the perspective of gradient regularization. The key idea is first to break the rote memories by network pruning to address memorization overfitting in the inner loop, then use the gradients of pruned sub-networks to augment meta-gradients, alleviating overfitting in the outer loop. Specifically, we explore three pruning strategies, including random width pruning, random parameter pruning, and a newly proposed catfish pruning that measures a Meta-Memorization Carrying Amount (MMCA) score for each parameter and prunes high-score ones to break rote memories. The proposed MGAug is theoretically guaranteed by the generalization bound from the PAC-Bayes framework. Extensive experiments on multiple few-shot learning benchmarks validate MGAug's effectiveness and significant improvement over various meta-baselines.

Incomplete multi-view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi-view data in real-world applications. However, existing methods often struggle to effectively deal with large-scale datasets, particularly those with a significant number of missing instances. To address these issues, we propose a novel method called Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View Data (HENRI). HENRI utilizes the consensus hubs of all views to identify informative anchors to handle large-scale incomplete datasets. Furthermore, it incorporates a novel sample-level fusion strategy that effectively integrates information from all views, leading to remarkable outcomes in both cluster formation and missing data reconstruction. HENRI demonstrates exceptional capability in capturing the underlying structures of the data and recovering missing information, even when faced with a significant number of instances with incomplete data in partial views. To validate its effectiveness, we conducted experiments on 6 complete datasets and 31 incomplete datasets, comparing against 11 baseline methods. The results are impressive, demonstrating the superior performance of HENRI over the state-of-the-art methods.

Deep multi-view subspace clustering aims to reveal a common subspace structure by exploiting rich multi-view information. Despite promising progress, current methods focus only on multi-view consistency and complementarity, often overlooking the adverse influence of entangled superfluous information in features. Moreover, most existing works lack scalability and are inefficient for large-scale scenarios. To this end, we innovatively propose a deep subspace clustering method via Multi-view Feature Decoupling (MvFD). First, MvFD incorporates well-designed multi-type auto-encoders with self-supervised learning, explicitly decoupling consistent, complementary, and superfluous features for every view. The disentangled and interpretable feature space can then better serve unified representation learning. By integrating these three types of information within a unified framework, we employ information theory to obtain a minimal and sufficient representation with high discriminability. Besides, we introduce a deep metric network to model self-expression correlation more efficiently, where network parameters remain unaffected by changes in sample numbers. Extensive experiments show that MvFD yields State-of-the-Art performance in various types of multi-view datasets.

Multiview spectral clustering, which stands out with its remarkable clustering performance, has drawn increasing research attention. Its core is properly weighing the contribution of different views and comprehensively utilizing multiview information in the clustering process. Although existing methods, like exponential decay and root loss, have achieved significant progress, they still have limitations in their weighting scheme, application generalization, and model efficiency. To handle these limitations, we propose a novel Similarity-Induced Weighted Consensus Laplacian matrix learning method for multiview clustering (MC), named SIWCL. This method has two distinctive features: 1) instead of conventional Laplacian matrix learning, SIWCL resorts to consensus Laplacian matrix learning as the MC framework for effectively exploiting complementary information from multiple views and 2) we argue that there could be outlier views that exhibit an uneven similarity distribution with other views, and equally treating them with other views can hurt model performance. Therefore, beyond consensus Laplacian matrix learning, SIWCL introduces a novel weighting strategy that adaptively assigns weights to views according to their consistency with other views based on the multiview similarity matrices. Notably, this novel method has only one hyperparameter and closed-form solutions, greatly improving the efficiency and generalization. Experiments show that the weights obtained by the proposed weighting strategy are correlated to the quality of the clustering structure. The comparisons between the proposed method and other state-of-the-art baseline methods over eight datasets demonstrate the robustness and superior clustering performance of SIWCL.

Multi-view (representation) learning derives an entity's representation from its multiple observable views to facilitate various downstream tasks. The most challenging topic is how to model unobserved entities and their relationships to specific views. To this end, this work proposes a novel multi-view learning method using a View-Aware parameter Modulation mechanism, termed VAM. The key idea is to use trainable parameters as proxies for unobserved entities and views, such that modeling entity-view relationships is converted into modeling the relationship between proxy parameters. Specifically, we first build a set of trainable parameters to learn a mapping from multi-view data to the unified representation as the entity proxy. Then we learn a prototype for each view and design a Modulation Parameter Generator (MPG) that learns a set of view-aware scale and shift parameters from prototypes to modulate the entity proxy and obtain view proxies. By constraining the representativeness, uniqueness, and simplicity of the proxies and proposing an entity-view contrastive loss, parameters are alternatively updated. We end up with a set of discriminative prototypes, view proxies, and an entity proxy that are flexible enough to yield robust representations for out-of-sample entities. Extensive experiments on five datasets show that the results of our VAM outperform existing methods in both classification and clustering tasks.

Time series research in academic and industrial fields has attracted wide attention. However, the frequency information contained in time series still lacks effective modeling. The studies found that time series forecasting relies on different frequency patterns: short-term series forecasting relies more on high-frequency components, while long-term forecasting focuses more on low-frequency data. To better describe the multifrequency mode, a dual-stage multifeature adaptive frequency domain prediction model (DMAFD) is proposed in this paper. DMAFD contains two stages. First, it adopts the XGBoost algorithm to obtain a feature vector by analyzing the feature importance. Second, the frequency feature extraction of time series and the frequency aware modeling of the target sequence is integrated, for building an end-to-end prediction network based on the dependence of time series on frequency mode. The innovation is reflected in the fact that the prediction network can automatically focus on multifrequency components according to the dynamic evolution of the input sequence. Extensive experiments on four real data sets from different fields show that DMAFD obtains higher accuracy and smaller lags in time step analysis compared with state-of-the-art algorithms.

As we all know, video frame rate determines the quality of the video. The higher the frame rate, the smoother the movements in the picture, the clearer the information expressed, and the better the viewing experience for people. Video interpolation aims to increase the video frame rate by generating a new frame image using the relevant information between two consecutive frames, which is essential in the field of computer vision. The traditional motion compensation interpolation method will cause holes and overlaps in the reconstructed frame, and is easily affected by the quality of optical flow. Therefore, this paper proposes a video frame interpolation method via optical flow estimation with image inpainting. First, the optical flow between the input frames is estimated via combined local and global-total variation (CLG-TV) optical flow estimation model. Then, the intermediate frames are synthesized under the guidance of the optical flow. Finally, the nonlocal self-similarity between the video frames is used to solve the optimization problem, to fix the pixel loss area in the interpolated frame. Quantitative and qualitative experimental results show that this method can effectively improve the quality of optical flow estimation, generate realistic and smooth video frames, and effectively increase the video frame rate.