Hypergraph Neural Networks

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.Comment: Accepted in AAAI'201

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.Comment: CVPR2017 Ora

Estudio de modelos innovadores de self media para el emprendimiento

[ES] El presente Trabajo Fin de Master tiene como objeto profundizar en la importancia y evolución que han desarrollado los sistemas self media en el entorno tan dinámico al que las organizaciones se enfrentan. Para ello, se ha realizado un análisis bibliográfico de diversos autores que han estudiado previamente la temática objeto del proyecto. Analizando las diversas herramientas de self media, las redes sociales han permitido que las empresas crezcan y conviertan la interacción con los consumidores en un sistema bilateral de comunicación. Además de la revisión literaria expuesta en la primera parte del proyecto, se han analizado una serie de casos de éxito que ejemplifican cómo los sistemas self media benefician a consumidores y empresas. Por último, se ha relacionado el crecimiento de la actividad emprendedora en España con la expansión de los sistemas self media a causa de la multitud de facilidades que aporta a los pequeños emprendedores que sin recursos consiguen mayores oportunidades en las plataformas de social media.[EN] The purpose of this Master's Final Project is to deepen the importance and evolution that self-media systems have developed in the dynamic environment that organizations face. For this, a bibliographic analysis of various authors who have previously studied the subject matter of the project has been carried out. Analyzing the various self media tools, social networks have allowed companies to grow and turn interaction with consumers into a two-way communication system. 5 In addition to the literary review presented in the first part of the project, a series of success stories have been analyzed that exemplify how self-media systems benefit consumers and companies. Finally, the growth of entrepreneurial activity in Spain has been related to the expansion of self-media systems due to the multitude of facilities it provides to small entrepreneurs who, without resources, get greater opportunities on social media platforms.Zhang, Z. (2021). Estudio de modelos innovadores de self media para el emprendimiento. Universitat Politècnica de València. http://hdl.handle.net/10251/17504

ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction

We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection overlook the structural constraints within linear map elements, resulting in performance degradation in long-range scenarios. In this paper, we exploit the properties of map elements to improve the performance of map construction. We extract more accurate bird's eye view (BEV) features guided by their linear structure, and then propose a hierarchical sparse map representation to further leverage the scalability of vectorized map elements and design a progressive decoding mechanism and a supervision strategy based on this representation. Our approach, ScalableMap, demonstrates superior performance on the nuScenes dataset, especially in long-range scenarios, surpassing previous state-of-the-art model by 6.5 mAP while achieving 18.3 FPS. Code is available at https://github.com/jingy1yu/ScalableMap