Multiple kernel clustering aims to exploit complementary information across multiple views by learning an optimal kernel combination. Despite its efficiency and simplicity, simple multiple kernel k-means remains sensitive to sample deviations and perturbations, which limits its robustness in practical applications. To address these issues, we propose an entropy regularized simple multiple kernel k-means with adaptive deviation correction (ERSMKKM-ADC). This method incorporates an entropy regularization term to balance the distribution of multiple kernel weights, preventing them from becoming overly sparse or excessively concentrated. Furthermore, it employs an adaptive deviation correction mechanism that dynamically adjusts the partition matrix via a soft-thresholding strategy, improving the robustness of the clustering results. Extensive experiments on public benchmark datasets demonstrate that ERSMKKM-ADC consistently outperforms state-of-the-art methods.

Tensor-based multi-view clustering is a popular approach. It can enhance representation learning by exploring higher-order correlations among views. However, two key issues remain unsolved. First, minimizing the tensor rank is a complex multi-objective optimization problem, so finding a suitable optimization strategy is an open problem. Moreover, most tensor methods require two phases to obtain the consensus matrix, which usually leads to suboptimal performance. To address these issues, we propose a Tensorial Multi-view Clustering via Alternative Rank Minimization and Inter-view Alignment (ARIA), in which multiple low-rank matrices and the consistent matrix are jointly optimized in a unified framework. Specifically, we stack the representations obtained from different views into a higher-order tensor. Then, a non-convex alternative rank-minimizing regularization is introduced to achieve a tighter approximation of the rank function. Besides, we impose intra-view alignment constraints to establish a connection between inter-view and intra-view. Unlike the previous method, it is a one-step strategy to obtain the consensus representation. Notably, our approach requires only linear complexity, and thus it can be successfully applied in large-scale clustering tasks. Extensive experiments validate the effectiveness and scalability of the proposed method. The code for ARIA is publicly available athttps://github.com/zskong/ARIA.

These years have witnessed remarkable progress in modeling user behaviour from personalized online services, especially knowledge graph-based recommendation systems. Meanwhile, more studies are focusing on aspects beyond recommendation performance, since such an observational data-driven paradigm is posing threats to both users and society in terms of trustworthiness. In fact, existing problem-oriented solutions still face significant challenges, as almost all of them suffer from the generality limitations to improve their trustworthiness in a uniform fashion. To address these issues, we propose a plug-and-playDebiasing framework forKnowledgeGraph-basedRecommendationSystems, also known as DiKGRS. Specifically, the Knowledge-augmented Pseudo-Samples Generation (KPSG) method, a novel data augmentation perspective, is proposed to explore more auxiliary information beyond observational user behaviors. Furthermore, the Debiasing Value Networks (DVN), is also developed to evaluate the reliability of generated pseudo-samples by modeling both the item popularity and user demographic bias in the platform. Moreover, an adaptive weighting coordination module is performed to coordinate the proposed DiKGRS framework and its backbones. Experimental results on four real-world datasets from different online service personalization scenarios have illustrated that the proposed framework can significantly improve the trustworthiness of existing knowledge graph-based recommendation systems. The code has been released public available at:https://github.com/alipay/A-Knowledge-augmented-Method-DiKGRS.

Despite the encouraging achievements, the practical application of recommendation systems still faces two key issues. The first is how to better understand the multimodal real-time requests that are the more mainstream request behavior in industrial scenarios; the other is how to effectively capture users' dynamic needs that change with temporal and spatial conditions. The breakthroughs in text understanding and generation capabilities of Large Language Models (LLMs) have demonstrated their tremendous potential in precise recommendation systems, particularly through the enhancement of the understanding of user intent. To address these issues, we propose a novel Spatial-Temporal Multimodal LLM for generative recommendation. Specifically, on the basis of the behavior data constructed from Alipay, spatial-temporal knowledge-guided fine-tuning module is proposed to capture specific needs in user real-time requests. Furthermore, a preference discovery module is developed to learn user preferences in visual queries from multimodal request perspective. Meanwhile, a personalized recommendation module is designed to aggregate spatial-temporal knowledge and user preferences for generative recommendation. Experimental results on a real-world deployed generative recommendation task from the ‘Explore' scenario in Alipay have demonstrated the effectiveness of the proposed framework.

Calibration is an essential prerequisite for the accurate data fusion of LiDAR and camera sensors. Traditional calibration techniques often require specific targets or suitable scenes to obtain reliable 2D-3D correspondences. To tackle the challenge of target-less and online calibration, deep neural networks have been introduced to solve the problem in a data-driven manner. While previous learning-based methods have achieved impressive performance on specific datasets, they still struggle in complex real-world scenarios. Most existing works focus on improving calibration accuracy but overlook the underlying mechanisms. In this paper, we revisit the development of learning-based LiDAR-Camera calibration and encourage the community to pay more attention to the underlying principles to advance practical applications. We systematically analyze the paradigm of mainstream learning-based methods, and identify the critical limitations of regression-based methods with the widely used data generation pipeline. Our findings reveal that most learning-based methods inadvertently operate as retrieval networks, focusing more on single-modality distributions rather than cross-modality correspondences. We also investigate how the input data format and preprocessing operations impact network performance and summarize the regression clues to inform further improvements.

Facing the challenges of sparsity and long tail in thousands of Mini-apps recommendation scenarios deployed on Alipay platform, there is a great need for a simple, effective, and easy-to-deploy industrial solution. To address this issue, we follow the strategy of “divide and conquer” and propose a crowd-based recommendation model by using D eterminantal P oint P rocesse s on C rowd-wise M ixture- o f- E xperts (DPPs-CMoE). Specifically, under the guidance of DPPs-based prototypical tags, the user profiling space is sequentially divided into multiple crowds, with each of them taking on a unique latent specificity; Meanwhile, by treating the modeling of crowd specificity as one of multiple tasks, a crowd-wise architecture is adopted to seamlessly unify the multiple expert networks from the overall user space and the gating network from each of independent crowd spaces. The effectiveness of the proposed method has been illustrated in the experimental results on a mini-apps recommendation scenario deployed in Alipay APPs.

Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks. Recently, several studies have embraced the multitask learning approach in the healthcare domain, exploiting the inherent correlations among clinical tasks to predict multiple outcomes simultaneously. However, existing methods necessitate samples to possess complete labels for all tasks, which places heavy demands on the data and restricts the flexibility of the model. Meanwhile, within a multitask framework with multimodal inputs, how to comprehensively consider the information disparity among modalities and among tasks still remains a challenging problem. To tackle these issues, a unified healthcare prediction model, also named by \textbf{FlexCare}, is proposed to flexibly accommodate incomplete multimodal inputs, promoting the adaption to multiple healthcare tasks. The proposed model breaks the conventional paradigm of parallel multitask prediction by decomposing it into a series of asynchronous single-task prediction. Specifically, a task-agnostic multimodal information extraction module is presented to capture decorrelated representations of diverse intra- and inter-modality patterns. Taking full account of the information disparities between different modalities and different tasks, we present a task-guided hierarchical multimodal fusion module that integrates the refined modality-level representations into an individual patient-level representation. Experimental results on multiple tasks from MIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the proposed method. Additionally, further analysis underscores the feasibility and potential of employing such a multitask strategy in the healthcare domain. The source code is available at https://github.com/mhxu1998/FlexCare.

Risk prediction, usually achieved by learning representations from patient’s physiological sequence or user’s behavioral sequence data, and has been widely applied in healthcare and finance. Despite that, some recent time-aware deep learning methods have led to superior performances in such sequence representation learning tasks, such improvement is limited due to a lack of guidance from hierarchical global view. To address this issue, we propose a novel end-to-end H ierarchical G lobal V iew-guided (HGV) sequence representation learning framework. Specifically, the Global Graph Embedding (GGE) module is proposed to learn sequential clip-aware representations from temporal correlation graph (TCG) at instance level. Furthermore, following the way of key-query attention, the harmonic β-attention (β-Attn) is also developed for making a global tradeoff between time-aware decay and observation significance at channel level adaptively. Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view. Experimental results on both healthcare risk prediction benchmark and SMEs credit overdue risk prediction task from the real-world industrial scenario in MYBank, Ant Group, have illustrated that the proposed model can achieve competitive prediction performance compared with other known baselines. The code has been released public available at: https://github.com/LiYouru0228/HGV.

While large enterprises are benefiting from their global supply chains in these years, it is not easy for Small and Medium-sized Enterprises (SMEs) to find supply chain partners. Treating it as a supply chain mining problem, some deep learning methods, especially knowledge graph (KG) enhanced ones, can achieve workable performance by utilizing explicit structure information from KG while considering effectiveness. However, such improvement is limited when facing the challenges of scalability, complexity, and noisiness in large-scale KGs. To address these issues, we propose a novelMeta-tagSupportedConnectivity representationLearning framework, also known as MSCL. Specifically, a Meta-tag Collaborative Filtering (MCF) method is proposed to highlight the representative schema from huge number of paths connecting two enterprises in large-scale KG. Furthermore, the DPPs-induced Hierarchical Path Sampling (DHPS), a novel sampling framework, is also developed to capture the latent connectivity pattern in KG more effectively. Moreover, the path-wise knowledge representations and the underlying information inherent in pairwise enterprises are aggregated by a connectivity representation learning (CRL) approach for SMEs supply chain mining. Experimental results from two real-world industries have illustrated that the proposed model can achieve competitive performance compared with other existing baselines.

To effectively explore the supply chain relationships among Small and Medium-sized Enterprises (SMEs), some remarkable progress in such a relation modeling problem, especially knowledge graph-based methods have been witnessed during these years. As a typical link prediction task, supply chain prediction can usually predict the unknown future relationship facts between SMEs by utilizing the historical semantic connections between entities in knowledge graphs (KGs). However, it is still a great challenge for existing models as seldom of them can consider both temporal dependency and cooperative correlation of the connectivity pattern along the timeline synergistically. Accordingly, we propose a novel framework to learn joint relational co-evolution in Spatial-Temporal Knowledge Graphs (STKG). Specifically, on the base of the constructed large-scale financial STKG, a multi-view relational sequences mining method is proposed to reveal the semantic information from ontological concepts. Furthermore, a relational co-evolution learning module is also developed to capture the regularity of evolving connectivity patterns from the spatial-temporal view. Meanwhile, a multiple random subspace representation learning layer is also designed to improve both compatibility and complementarity during knowledge aggregation. Experimental results on large-scale SMEs supply chain prediction tasks from four real-world industries in China have illustrated the effectiveness of the proposed model.

Parking slot detection is an essential yet challenging task in the field of self-driving perception. During parking, vehicles often block part of the parking slots which makes the corners occluded. In addition, due to the impact of the external environment, the corners of the parking slot may be blurred. Existing parking slot detection algorithms based on parking slot markings are sensitive to the corners of the parking slots, which makes it difficult to cope with the above scenario. To address this problem, we propose a parking slot entrance line detection algorithm called RPSED, which is the first to apply rotating object detection to the parking slot entrance line. RPSED takes a different route from traditional corner detection methods by focusing on the entrance lines of parking slots to grasp the intricate geometric details inherent to parking slots, which solves the problem that existing parking slot detection algorithms cannot detect parking slots with blurred corners. To further improve the precision and recall of the model and make the model more generalizable, we propose a model ensemble strategy to match and select the results of multiple models. Moreover, we propose two manually optimized parking slot dataset named RPS2.0 and RPSV, which adds more annotations with obstructed corners or obscured configurations to the datasets ps2.0 and psv, making the model evaluation more reasonable and realistic. Experimental results on the RPS2.0 and RPSV benchmarks demonstrate the superiority of our approach compared to existing state-of-the-art methods.

For an actual recommendation system, it generally involves a variety of heterogeneous interactive relationships, such as the typical user-user (U2U), item-item (I2I), and user-item (U2I) interaction relationships. With the application of graph neural networks (GNNs) in embedding various interactive relations, recommendation technology has made gratifying progress in recent years, which benefits lot from its powerful ability in relation modeling. However, most of the existing GNN-based methods fail to collaboratively explore the above heterogeneous multiple interactive relationships, including the internal correlations among multiple relationships and the intrinsic association behind different relationships. As a consequence, the user's personalized preference for the items to be recommended will not be well captured. In this paper, we propose aSylvester equation inducedCollaborativeRepresentationLearning framework (S-CRL) for recommendation system by utilizing the heterogeneous multiple interactive relationships. In particular, we ingeniously define a novel Sylvester equation to associate tactfully the multiple heterogeneous relations together. From the perspective of rating propagation, such Sylvester equation is shown theoretically to be the optimal solution of a local structure sensitive rating propagation function. Additionally, to seek more expressive embeddings about user and item, a layer-wise attention is introduced to aggregate the multi-hop information from U2U and I2I graphs, respectively, so as to promote the aggregation with the corresponding embeddings from the U2I interaction graph. Extensive experiments on three real-world datasets verify that our model achieves more favorable performance over currently representative methods.

Currently, most of the adversarial attacks focused on perturbation adding on 2D images. In this way, however, the adversarial attacks cannot easily be involved in a real-world AI system, since it is impossible for the AI system to open an interface to attackers. Therefore, it is more practical to add perturbation on real-world 3D objects’ surface, i.e., 3D adversarial attacks. The key challenges for 3D adversarial attacks are how to effectively deal with viewpoint changing and keep strong transferability across different state-of-the-art networks. In this paper, we mainly focus on improving the robustness and transferability of 3D adversarial examples generated by perturbing the surface textures of 3D objects. Towards this end, we propose an effective method, named Momentum Gradient-Filter Sign Method (M-GFSM), to generate 3D adversarial examples. Specially, the momentum is introduced into the procedure of 3D adversarial examples generation, which results in multiview robustness of 3D adversarial examples and high efficiency of attacking by updating the perturbation and stabilizing the update directions. In addition, filter operation is involved to improve the transferability of 3D adversarial examples by filtering gradient images selectively and completing the gradients of neglected pixels caused by downsampling in the rendering stage. Experimental results show the effectiveness and good transferability of the proposed method. Besides, we show that the 3D adversarial examples generated by our method still be robust under different illuminations.

360 videos provide an omnidirectional view of the scene with extremely large data. Therefore, representing 360 videos with less data has become more and more important. Cube format is such a popular representation of 360 videos. However, we have to convert cube to Equirectangula(ERP) for displaying convenience. In this paper, we enhance Cube-to-ERP conversion performance by joint using Convolutional Neural Network(CNN) and classical interpolation method. The optimal threshold of boundary is derived according to geometry features of the cube-to-ERP format. This threshold is the guidance of how to combine CNN and classical interpolation method. Our experiment results prove that the derived threshold has a certain degree of guiding significance. Furthermore, we propose a new evaluation criterion with the help of Marsaglia model. It is much easier and more accurate to evaluate geometry conversion process.

In HEVC, bit-allocation model is based on the hierarchical control, which can divide video sequence into three levels: Group of Picture (GOP), frame and Coding Tree Unit (CTU). However, the fixed size of GOP fails to consider the influence of scene change in the video coding process, which may decrease the compression efficiency and reconstructed quality. In this paper, the main idea of the proposed algorithm is to detect the scene change efficiently, and then apply it in the rate control algorithm of HEVC to decrease the BD-rate and save the coding time.

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.

A growing number of multi-view data arises naturally in many scenarios, including medical diagnosis, webpage classification, and multimedia analysis. A challenge in learning from multi-view data is that not all instances are fully represented in all views, resulting in missing view data. In this paper, we focus on feature-level completion for missing view of multi-view data. Aiming at capturing both semantic complementarity and identical distribution among different views, an Isomorphic Linear Correlation Analysis (ILCA) method is proposed to linearly map multi-view data to a feature-isomorphic subspace through learning a set of excellent isomorphic features, thereby unfolding the shared information from different views. Meanwhile, we assume that missing view obeys normal distribution. Then, the missing view data matrix can be modeled as a low-rank component plus a sparse contribution. Thus, to accomplish missing view completion, an Identical Distribution Pursuit Completion (IDPC) model based on the learned features is proposed, in which the identical distribution constraint of missing view to the other available one in the feature-isomorphic subspace is fully exploited. Comprehensive experiments on several multi-view datasets demonstrate that our proposed framework yields promising results.

Summary form only given. This paper mainly studies currently developing 3D video coding based on HEVC. HEVC-based 3D video coding mainly focuses on 3DTV and auto-stereoscopic video compression system. A variety of new encoding tools, such as inter-view motion prediction and depth modeling modes, have been added in 3D-HEVC. Although 3D-HEVC provides greater bit rate saving, it also brings the enormous encoding complexity increase. The coding time is increased correspondingly. It is necessary to reduce the encoding time. In this paper, a fast CU-sized partition algorithm is proposed for 3D-HEVC intra coding. The key point of this algorithm is to find the relationship between the texture characteristic and the sub-partition in each CU. It needs to determine whether the LCU can be subdivided to smaller CU according to the relationship. In order to reduce the redundancy of the human eye, just noticeable difference (JND) is a high efficiency model in the base of psychology and physiology. Instead of the time-consuming rate distortion optimization for coding mode decision, the variance of JND in each CU can be exploited to partition the coding unit according to human visual system characteristics. In other words, the larger blocks with higher JND variance will be subdivided to smaller blocks with lower JND variance. Consequently, the rules of CU preliminary partition are decided as follows: (a) For a 64×64 CU, if the variance of JND is larger than 0.25, the CU will be sub-divided into four 32×32 sub-blocks. (b) For a 32×32 CU, if the variance of JND is larger than 0.15, the CU will be sub-divided into four 16×16 sub-blocks. (c) For a 16×16 CU, if the variance of JND is larger than 0.10, the CU will be sub-divided into four 8×8 sub-blocks. The proposed algorithm is implemented based on HTM-13.1 reference software. The experiment condition is set up as "All Intra-Main" (AI-Main) configuration [1]. The quantization parameter (QP) values of texture are set to 25, 30, 35 and 40, respectively and the corresponding QPs of depth can be set to 34,39,42,45. The experimental results show that the fast intra mode decision algorithm provides over 29.25% encoding time saving on average with comparable rate distortion performance.

In this article, we investigate the cross-media retrieval between images and text, that is, using image to search text (I2T) and using text to search images (T2I). Existing cross-media retrieval methods usually learn one couple of projections, by which the original features of images and text can be projected into a common latent space to measure the content similarity. However, using the same projections for the two different retrieval tasks (I2T and T2I) may lead to a tradeoff between their respective performances, rather than their best performances. Different from previous works, we propose a modality-dependent cross-media retrieval (MDCR) model, where two couples of projections are learned for different cross-media retrieval tasks instead of one couple of projections. Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I). Extensive experiments show the superiority of the proposed MDCR compared with other methods. In particular, based on the 4,096-dimensional convolutional neural network (CNN) visual feature and 100-dimensional Latent Dirichlet Allocation (LDA) textual feature, the mAP of the proposed method achieves the mAP score of 41.5%, which is a new state-of-the-art performance on the Wikipedia dataset.

With the development of 3D video technology, many studies have paid attention to compression efficiency and rate distortion performance. When 3D videos are transmitted over error-prone channels, they may suffer significant quality degradation. In this paper, we combine multiview video coding (MVC) with multiple description coding (MDC) for robust transmission. The proposed scheme can give full consideration of both intra-view and inter-view correlation for better estimation. Furthermore, an adaptive mode decision is designed to generate a label as redundant information. The experiments show that the redundant information occupies just a few bits while the PSNR values of the reconstructed videos demonstrate a significant improvement.

High efficiency video coding (HEVC) is an emerging video compression standard, developed by the Joint Collaborative Team on Video Coding (JCT-VC). The aim of HEVC standardization effort is to save about 50% bit rate for equal perceptual video quality relative to H.264/AVC. Although HEVC provides greater bit rate saving, it also brings the enormous encoding complexity increase. In this paper, we propose a fast intra CU decision algorithm based on the texture characteristics of video. Furthermore, we also consider the coding bits of each CU as auxiliary information to refine the partition results. Experimental results show that the fast intra mode decision algorithm provides over 33% complexity reduction in terms of encoding time with negligible quality loss, compared with the original HEVC test model version HM-12.0+RExt-4.0rc2.

Traditional clustering and feature selection methods consider the data matrix as static. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is to analyze them separately. Such strategy ignores the time-dependent nature of the underlying data. In this paper, we propose two formulations for evolutionary co-clustering and feature selection based on the fused Lasso regularization. The evolutionary co-clustering formulation is able to identify smoothly varying hidden block structures embedded into the matrices along the temporal dimension. Our formulation is very flexible and allows for imposing smoothness constraints over only one dimension of the data matrices. The evolutionary feature selection formulation can uncover shared features in clustering from time-evolving data matrices. We show that the optimization problems involved are non-convex, non-smooth and non-separable. To compute the solutions efficiently, we develop a two-step procedure that optimizes the objective function iteratively. We evaluate the proposed formulations using the Allen Developing Mouse Brain Atlas data. Results show that our formulations consistently outperform prior methods.

In this paper, a novel scheme of two-stage multiview image compression is proposed to create two-level reconstructed quality. Differently from the conventional multiview image compression algorithms, SIFT (Scale-Invariant Feature Transform) features matching from interview images are exploited to remove the correlations between multiple views. In the first stage coding, SIFT and RANSAC (RANdom SAmple Consensus) algorithms are combined to calculate the correlation matrix of interview, which then can be developed to obtain the coarse reconstruction of the current view. In the second stage coding, the reconstructed quality can be improved further by using the residual information. The experimental results have shown that at higher compression ratio, the proposed scheme can obtain better rate-distortion performance than intra coding in MVC (Multiview Video Coding). Furthermore, with the change of the compression ratio, the proposed scheme can achieve more stable reconstructed quality.

In some real world applications, like information retrieval and data classification, we often are confronted with the situation that the same semantic concept can be expressed using different views with similar information. Thus, how to obtain a certain Semantically Consistent Patterns (SCP) for cross-view data, which embeds the complementary information from different views, is of great importance for those applications. However, the heterogeneity among cross-view representations brings a significant challenge on mining the SCP. In this paper, we propose a general framework to discover the SCP for cross-view data. Specifically, aiming at building a feature-isomorphic space among different views, a novel Isomorphic Relevant Redundant Transformation (IRRT) is first proposed. The IRRT linearly maps multiple heterogeneous low-level feature spaces to a high-dimensional redundant feature-isomorphic one, which we name as mid-level space. Thus, much more complementary information from different views can be captured. Furthermore, to mine the semantic consistency among the isomorphic representations in the mid-level space, we propose a new Correlation-based Joint Feature Learning (CJFL) model to extract a unique high-level semantic subspace shared across the feature-isomorphic data. Consequently, the SCP for cross-view data can be obtained. Comprehensive experiments on three data sets demonstrate the advantages of our framework in classification and retrieval.

Multiple description coding (MDC) is a promising technology for robust transmission over error-prone channels, which has attracted a lot research interests. The basic idea of MDC is to how to utilize redundant information of the descriptions for robust transmission. In view of practical applications, many MDC approaches have been proposed compatible with a certain standard codec, especially H.264/AVC. In this paper, we attempt to develop a novel MD video codec with generalized compatibility, which aims to the effective redundancy allocation from inter-/intra-descriptions. In [1], the redundancy allocation may be not enough effective due to frame level. As a result, in this paper, the redundant information will be taken into account at MB level.

Analysis on click-through data from a very large search engine log shows that users are usually interested in the top-ranked portion of returned search results. Therefore, it is crucial for search engines to achieve high accuracy on the top-ranked documents. While many methods exist for boosting video search performance, they either pay less attention to the above factor or encounter difficulties in practical applications. In this paper, we present a flexible and effective reranking method, called CR-Reranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Reranking employs a cross-reference (CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to rerank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. Experimental results show that the search quality, especially on the top-ranked results, is improved significantly.

In this paper, we attempt to overcome the limitation of specific scalable video codec and apply FEC-MDC to a common video coder, such as the standard H.264. The proposed scheme is explained as follows. Firstly, according to motion vector changes, an original video sequence is divided into several sub-sequences as messages, so in each message better temporal correlation can be maintained for better estimation when information losses occur. Secondly, the standard H.264 encoder is used to encode the messages. Thirdly, based on priority encoding transmission, unequal protections are assigned in each message. Lastly, at the decoder, the segments whose priorities are not higher than the fraction of packets received can be recover totally.