Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has led to their increased adoption in recommender systems. By incorporating users and items into the knowledge graph, these systems can better capture the implicit connections between them and provide more accurate recommendations. In this paper, we investigate and propose the construction of a personalized recommender system via knowledge graphs embedding applied to the vehicle purchase/sale domain. The results of our experimentation demonstrate the efficacy of the proposed method in providing relevant recommendations that are consistent with individual users.
Dispersed particle size measurement is crucial in a variety of applications, be it in the sizing of spray droplets, tracking of particulate matter in multiphase flows, or the detection of target markers in machine vision systems. Further to sizing, such systems are characterised by extracting quantitative information like spatial position and associated velocity of the dispersed phase particles. In the present study we propose an imaging based volumetric measurement approach for estimating the size and position of spherically dispersed particles. The approach builds on the 'Depth from Defocus' (DFD) technique using a single camera approach. The simple optical configuration, consisting of a shadowgraph setup and a straightforward calibration procedure, makes this method readily deployable and accessible for broader applications.
While LiDAR sensors have been succesfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusiong radars and cameras for 3D object detection. However, previous radar-camera fusion models have not been able to fully utilize radar information in that initial 3D proposals were generated based on the camera features only and the instance-level fusion is subsequently conducted. In this paper, we propose radar-camera multi-level fusion (RCM-Fusion), which fuses radar and camera modalities at both the feature-level and instance-level to fully utilize radar information. At the feature-level, we propose a Radar Guided BEV Encoder which utilizes radar Bird's-Eye-View (BEV) features to transform image features into precise BEV representations and then adaptively combines the radar and camera BEV features. At the instance-level, we propose a Radar Grid Point Refinement module that reduces localization error by considering the characteristics of the radar point clouds. The experiments conducted on the public nuScenes dataset demonstrate that our proposed RCM-Fusion offers 11.8% performance gain in nuScenes detection score (NDS) over the camera-only baseline model and achieves state-of-the-art performaces among radar-camera fusion methods in the nuScenes 3D object detection benchmark. Code will be made publicly available.
OD matrix estimation is a critical problem in the transportation domain. The principle method uses the traffic sensor measured information such as traffic counts to estimate the traffic demand represented by the OD matrix. The problem is divided into two categories: static OD matrix estimation and dynamic OD matrices sequence(OD sequence for short) estimation. The above two face the underdetermination problem caused by abundant estimated parameters and insufficient constraint information. In addition, OD sequence estimation also faces the lag challenge: due to different traffic conditions such as congestion, identical vehicle will appear on different road sections during the same observation period, resulting in identical OD demands correspond to different trips. To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization. Our experiments show that the neural network(NN) can effectively infer the structure of the OD sequence and provide practical constraints for numerical optimization to obtain better results. Moreover, the experiments show that provided structural information contains not only constraints on the spatial structure of OD matrices but also provides constraints on the temporal structure of OD sequence, which solve the effect of the lagging problem well.
Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, several previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes, e.g. identity, hairstyle, pose, or expression. Most of these works require designing special loss functions and training dedicated network architectures. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN's latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in state-of-the-art pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. The variations in our generated images are not limited to specific attributes as ExFaceGAN explicitly aims at disentangling identity information, while other visual attributes are randomly drawn from a learned GAN latent space. As an example of the practical benefit of our ExFaceGAN, we empirically prove that data generated by ExFaceGAN can be successfully used to train face recognition models.
Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Biomedical image analysis is a widely known solution to diagnose brain tumor. Although MRI is the current standard method for imaging tumors, its clinical usefulness is constrained by the requirement of manual segmentation which is time-consuming. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools and the UNet architecture is commonly used for segmentation. However, the traditional UNet has limitations in terms of complexity, training, accuracy, and contextual information processing. As a result, the modified UNet architecture, which incorporates residual dense blocks, layered attention, and channel attention modules, in addition to stacked convolution, can effectively capture both coarse and fine feature information. The proposed SLCA UNet approach achieves good performance on the freely accessible Brain Tumor Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845, 0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for BraTS 2020 dataset, respectively.
Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural Language Processing (NLP) is a cross-discipline of AI and Linguistics, dedicated to study the understanding of the text. This is a very challenging area due to unstructured nature of the language, with many ambiguous and corner cases. In this thesis we address a very specific area of NLP that involves the understanding of entities (e.g., names of people, organizations, locations) in text. First, we introduce a radically different, entity-centric view of the information in text. We argue that instead of using individual mentions in text to understand their meaning, we should build applications that would work in terms of entity concepts. Next, we present a more detailed model on how the entity-centric approach can be used for the entity linking task. In our work, we show that this task can be improved by considering performing entity linking at the coreference cluster level rather than each of the mentions individually. In our next work, we further study how information from Knowledge Base entities can be integrated into text. Finally, we analyze the evolution of the entities from the evolving temporal perspective.
Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.
Meta-structures are widely used to define which subset of neighbors to aggregate information in heterogeneous information networks (HINs). In this work, we investigate existing meta-structures, including meta-path and meta-graph, and observe that they are initially designed manually with fixed patterns and hence are insufficient to encode various rich semantic information on diverse HINs. Through reflection on their limitation, we define a new concept called meta-multigraph as a more expressive and flexible generalization of meta-graph, and propose a stable differentiable search method to automatically optimize the meta-multigraph for specific HINs and tasks. As the flexibility of meta-multigraphs may propagate redundant messages, we further introduce a complex-to-concise (C2C) meta-multigraph that propagates messages from complex to concise along the depth of meta-multigraph. Moreover, we observe that the differentiable search typically suffers from unstable search and a significant gap between the meta-structures in search and evaluation. To this end, we propose a progressive search algorithm by implicitly narrowing the search space to improve search stability and reduce inconsistency. Extensive experiments are conducted on six medium-scale benchmark datasets and one large-scale benchmark dataset over two representative tasks, i.e., node classification and recommendation. Empirical results demonstrate that our search methods can automatically find expressive meta-multigraphs and C2C meta-multigraphs, enabling our model to outperform state-of-the-art heterogeneous graph neural networks.
Skin lesion segmentation (SLS) plays an important role in skin lesion analysis. Vision transformers (ViTs) are considered an auspicious solution for SLS, but they require more training data compared to convolutional neural networks (CNNs) due to their inherent parameter-heavy structure and lack of some inductive biases. To alleviate this issue, current approaches fine-tune pre-trained ViT backbones on SLS datasets, aiming to leverage the knowledge learned from a larger set of natural images to lower the amount of skin training data needed. However, fully fine-tuning all parameters of large backbones is computationally expensive and memory intensive. In this paper, we propose AViT, a novel efficient strategy to mitigate ViTs' data-hunger by transferring any pre-trained ViTs to the SLS task. Specifically, we integrate lightweight modules (adapters) within the transformer layers, which modulate the feature representation of a ViT without updating its pre-trained weights. In addition, we employ a shallow CNN as a prompt generator to create a prompt embedding from the input image, which grasps fine-grained information and CNN's inductive biases to guide the segmentation task on small datasets. Our quantitative experiments on 4 skin lesion datasets demonstrate that AViT achieves competitive, and at times superior, performance to SOTA but with significantly fewer trainable parameters. Our code is available at https://github.com/siyi-wind/AViT.