This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordin...
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This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordina...
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In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead...
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Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajector...
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The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, a...
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With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently ...
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We consider the task of multi-view subspace learning which integrates multi-view information to learn a unified representation for multimedia data. In real-world scenarios, we encounter views with high diversities of ...
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ISBN:
(纸本)9781509065509
We consider the task of multi-view subspace learning which integrates multi-view information to learn a unified representation for multimedia data. In real-world scenarios, we encounter views with high diversities of semantic levels. Neglecting the problem of semantic inconsistency, existing graph-based methods directly convert heterogeneous information into local affinity matrices to conduct a fusion process, which inevitably destroys the valuable high-semantic-level structure. To address semantic inconsistency, we propose Multi-view Subspace Skeleton Embedding (MSSE), in which the high-level semantic structure of the learned subspace is explicitly taken as the skeleton of the learned subspace. Specifically, cooperating with a set of anchor points, the high-level semantic structure is adopted as semantic constraints to guide the multi-graph learning process based on RESCAL tensor factorization. To guarantee sufficient geometric coverage of the skeleton in the learned subspace, we enforce the diversity of anchor points by a Determinantal Point process (DPP) regularizer. Compared with traditional methods, the learned subspace is endowed with higher semantic consistency and more robust to noisy views. Experiments on real-world image datasets demonstrate the promising performance comparing to state-of-the-art graph-based methods.
This paper presents a Cross-Modal Online Low-Rank Similarity function learning method (CMOLRS) for cross-modal retrieval, which learns a low-rank bilinear similarity measure on data from different modalities. CMOLRS m...
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ISBN:
(纸本)9781509060689
This paper presents a Cross-Modal Online Low-Rank Similarity function learning method (CMOLRS) for cross-modal retrieval, which learns a low-rank bilinear similarity measure on data from different modalities. CMOLRS models the cross-modal relations by relative similarities on a set of training data triplets and formulates the relative relations as convex hinge loss functions. By adapting the margin of hinge loss using information from feature space and label space for each triplet, CMOLRS effectively captures the multi-level semantic correlation among cross-modal data. The similarity function is learned by online learning in the manifold of low-rank matrices, thus good scalability is gained when processing large scale datasets. Extensive experiments are conducted on three public datasets. Comparisons with the state-of-the-art methods show the effectiveness and efficiency of our approach.
This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We a...
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ISBN:
(纸本)9781509060689
This paper proposes a novel method for cross-modal retrieval. Different from vector (text)-to-vector (image) framework of the traditional cross-modal methods, we adopt a vector (text)-to-matrix (image) framework. We assume that compared with vectors, matrices can directly represent images and characterize the structure of feature space. Furthermore, we propose a Metric based on Multi-order spaces (MMs). Multi-order statistic features are used to represent images for enriching the semantic information, and metrics among the multi-spaces are jointly learned to measure the similarity between two different modalities. Specifically, there are three steps for MMs. First, we jointly use the bags of visual features (zero-order), mean (first-order) and covariance (second-order) to characterize each image. Second, considering that covariance matrices and vectors lie on a Riemannian manifold and an Euclidean space respectively, we embed multi-order spaces into their corresponding Hilbert spaces to reduce the heterogeneity among the original spaces. Finally, the similarity between two different modalities can be measured by learning multiple transformations from the different Hilbert spaces to a common subspace. The performance of the proposed method over the state-of-the-art has been demonstrated through the experiments on two public datasets.
In this paper, we present a scene text extraction approach which can realize text localization and segmentation simultaneously. Two popular paradigms (machine learning method and rule-based method) are combined to ach...
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