Text-based person search aims to retrieve the most relevant pedestrian images from an image gallery based on textual descriptions. Most existing methods rely on two separate encoders to extract the image and text features, and then elaborately design various schemes to bridge the gap between image and text modalities. However, the shallow …
قرأ أكثر
There are many generalized rough set methods available, including the IV-MG-PRFS models. When we consider the multi-granularity circumstances, the mean multi-granularity, optimistic multi-granularity, and pessimistic multi-granularity could be generated. Whether (x_{8}) needs to receive treatment depends on which model the …
قرأ أكثر
We also design a gate mechanism based on market-aware technical indicators to fuse the multi-granularity features at each time step adaptively. Extensive experiments on three real-world datasets show significant improvements of our approach over the state-of-the-art baselines on stock trend prediction and profitability in real investing scenarios.
قرأ أكثر
Multi-Granularity transformer layer. As it is illustrated in Fig. 2, the proposed MGT is built upon the novel multi-granularity transformer layer (MGTL) which involves two sub-layers: multi-granularity self-attention layer and cross-token feedforward layer. There are two main differences between our MGTL and the existing transformer …
قرأ أكثر
In the real-world 24/7 surveillance systems, the images collected during the day and night are visible light images and infrared images, respectively. Infrared images lack color and texture information. In this case, it is more practical to use cross-modality person re-identification (re-ID) to process visible-infrared images. In fact, the cross …
قرأ أكثر
To overcome these challenges, we propose a multi-granularity-aware network (MGA-Net). Specifically, we design a multi-granularity hybrid feature fusion module (MGHF2M) to extract more representative local detail and global semantic information, enhancing the model's capability to represent ship features to adapt to …
قرأ أكثر
Software systems are a typical kind of man-made complex systems. Understanding their evolutions can lead to better software engineering practices. In this paper, the authors use complex network theory as a tool to analyze the evolution of object-oriented (OO) software from a multigranularity perspective. First, a multi-granularity …
قرأ أكثر
We design a transformer encoder-based model which abstracts sentence-level semantics from word-level semantics by removing the multi-granularity semantics representations. That is to say the inner-chunk self-attention layer and the inter-chunk self-attention layer are replaced by the inter-word self-attention layer.
قرأ أكثر
Mathematical modeling and optimization of the design parameters of the working chamber and the executive body (roll) of a single-roll gyratory shaft crusher, …
قرأ أكثر
We design a Transformer-based network named Multi-granularity Aggregation Transformer (MAT) to dynamically learn the complementary information between sub-aperture images in this paper. MAT is mainly implemented with the proposed multi-granularity aggregation blocks, which process sub-aperture images with three different …
قرأ أكثر
The Multiple Granularity protocol enhances concurrency and reduces lock overhead. It maintains the track of what to lock and how to lock. It makes easy to decide either to lock a data item or to unlock a data item. This type of hierarchy can be graphically represented as a tree. For example: Consider a tree which has four levels of nodes.
قرأ أكثر
This paper proposes a reconfigurable high performance multiplier (RHPM) based on multi-granularity design and parallel acceleration. Capable of supporting multiple precisions for different processing requirements, the RHPM can perform one 32×32, two 16×16, or four 8×8 bit unsigned/signed multiplication, or one 16×16, or two 8×8 bit …
قرأ أكثر
In this work, we propose to learn multi-granularity consecutive user intent unit to improve the recommendation performance. Specifically, we creatively propose Multi-granularity Intent Heterogeneous Session Graph (MIHSG) which captures the interactions between different granularity intent units and relieves the burden of long-dependency.
قرأ أكثر
The thinking mode based on granule structure in granular computing essentially simulates the pattern of human thinking to solve problem. Such thinking for the study of knowledge discovery is also of significant importance in cognitive computing. Under such circumstances, the theory of multi-granularity formal concept analysis (MG-FCA) …
قرأ أكثر
a Multi-granularity Intent Heterogeneous Session Graph (MIHSG). In this heterogeneous graph [39], nodes (CIUs) with different num-bers of items are categorized into different groups and the tran-sition edges among the same type of nodes capture the spatial continuity of the user-item interactions in the corresponding in-tent granularity.
قرأ أكثر
In this paper, we design a Matrix Capsule-based multi-granularity framework (MaCa) for this task. Specifically, we first introduce a word-level encoder to obtain the token-aware representations. Then, two sentence-level extractors are used to generate emotion prediction and cause prediction.
قرأ أكثر
Because the abstracts contain complex information and the labels of abstracts do not contain information about categories, it is difficult for cognitive models to extract comprehensive features to match the corresponding labels. In this paper, a cognitively inspired multi-granularity model incorporating label information (LIMG) is …
قرأ أكثر
for super-resolution [11], [12], we design a Transformer-based RSISR method termed Multi-granularity Backprojection Transformer (MBT). Specifically, we employ the backpro-jection learning strategy to learn low-resolution feature rep-resentations at different granularities. Firstly, we design the
قرأ أكثر
Multi-granularity structural information is shown to be effective for document-level biomedical relation extraction by some analytical experiments. ... these methods require a significant level of manual design. In recent years, deep neural networks have been widely used for sentence-level RE without feature engineering. Convolutional …
قرأ أكثر
This paper introduces a Multi-Granularity based Domain Design Space Exploration tool (MG-DmDSE) to improve both average application throughput as well as platform generality. The key contributions of MG-DmDSE are: (1) Applying a multi-granular decomposition of coarse grain application functions into more granular compute kernels. ...
قرأ أكثر
Abstract. Graph contrastive learning is an unsupervised learning method for graph data. It aims to learn useful representations by maximizing the similarity between similar instances and minimizing the similarity between dissimilar instances. Despite the success of the existing GCL methods, they generally overlook the hierarchical structures …
قرأ أكثر
Download : Download high-res image (341KB) Download : Download full-size image Fig. 2. General procedure of the proposed MGCEA with three core components, including multi-interval sampling (dividing decision variables into noncritical ones and critical ones), multi-granularity clustering (dividing decision variables into multiple …
قرأ أكثر
In this paper, an improved new design support system characterized by multigranularity is proposed. Previous studies has focused on only visualization of non-do Development of …
قرأ أكثر
In Section 3, the processes used to design multi-dimensional information granules, using the principle of justifiable granularity, are explained in detail, and the correlation index is introduced. In Section 4, the method for processing inhibitory data in multi-dimensional space is presented, and the effects of confidence levels on …
قرأ أكثر
Novel Design of a Modular Multi-stage Crusher with Adaptive Clearance by TRIZ Method | SpringerLink. Home. Advances in Mechanical Design. Conference …
قرأ أكثر
To this end, we further design a novel multi-granularity regularization term that enables the model to consider the correlations of classes in addition to re-balancing the data. A class hierarchy is first constructed by ontology or grouping semantically or visually similar classes. The multi-granularity regularization then transforms the one ...
قرأ أكثر
In recent years, many works have concentrated on designing models from the perspective of using the information of the question and options at a large granularity level. However, few studies have explored how the model uses the information to find the correct answer at a fine granularity level or a multi-granularity level.
قرأ أكثر
A method of DR multi-granularity modeling with two-stage aggregation is proposed, by which the resource granularity is increased and dynamic design capability …
قرأ أكثر
Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis, ignoring disease-level semantic correspondences. In this paper, we present a novel Multi …
قرأ أكثر
A multi-granularity feasibility evaluation model of the PDD was constructed based on the complex product's hierarchical structure, which not only described the evaluation indices from the product level to the component level but also presented methods and rules to quantify them. ... Aguiar J, Oliverira SJ (2017) A design tool to …
قرأ أكثر
Figure 1 demonstrates an overview of our method to assess BPC between process models and regulatory documents at design-time, which consists of three parts. (1) Process Model Disassembly This step involves extracting semantic components and control flow details from the labels associated with structured process models. (2) Regulatory …
قرأ أكثر