Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to gene...Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. The guided summary has an explicitly defined structure which comes from the important aspects of products. The proposed framework attempts to maximize expected aspect satisfaction during summary generation. The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation. Empirical experimental results on consumer reviews of cars show the effectiveness of our method.展开更多
In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complem...In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet.展开更多
Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream task...Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream tasks such as robotic grasping.Existing methods fail when the template and source images have different modalities,cluttered backgrounds,or weak textures.They also rarely consider geometric transformations via homographies,which commonly exist even for planar industrial parts.To tackle the challenges,we propose an accurate template matching method based on differentiable coarse-tofine correspondence refinement.We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image,allowing robust matching.An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers.This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation.Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines,providing good generalization ability and visually plausible results even on unseen real data.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.60973104 and 60803075with the aid of a grant from the International Development Research Center,Ottawa,Canada IRCI Project
文摘Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. The guided summary has an explicitly defined structure which comes from the important aspects of products. The proposed framework attempts to maximize expected aspect satisfaction during summary generation. The importance of an aspect to a generated summary is modeled using Labeled Latent Dirichlet Allocation. Empirical experimental results on consumer reviews of cars show the effectiveness of our method.
基金supported by the National Natural Science Foundation of China(No.61976023)。
文摘In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet.
基金supported in part by the National Key R&D Program of China(2018AAA0102200)the National Natural Science Foundation of China(62002375,62002376,62325221,62132021).
文摘Template matching is a fundamental task in computer vision and has been studied for decades.It plays an essential role in manufacturing industry for estimating the poses of different parts,facilitating downstream tasks such as robotic grasping.Existing methods fail when the template and source images have different modalities,cluttered backgrounds,or weak textures.They also rarely consider geometric transformations via homographies,which commonly exist even for planar industrial parts.To tackle the challenges,we propose an accurate template matching method based on differentiable coarse-tofine correspondence refinement.We use an edge-aware module to overcome the domain gap between the mask template and the grayscale image,allowing robust matching.An initial warp is estimated using coarse correspondences based on novel structure-aware information provided by transformers.This initial alignment is passed to a refinement network using references and aligned images to obtain sub-pixel level correspondences which are used to give the final geometric transformation.Extensive evaluation shows that our method to be significantly better than state-of-the-art methods and baselines,providing good generalization ability and visually plausible results even on unseen real data.
