Search Results for author:
Found 23 papers, 6 papers with code
A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation
We design a three-branch network to predict semantic labels, point offsets and extract deep embedding features.
A Lightweight Reconstruction Network for Surface Defect Inspection
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics.
Self-supervised Amodal Video Object Segmentation
The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.
DeepMLE: A Robust Deep Maximum Likelihood Estimator for Two-view Structure from Motion
In addition, in order to increase the robustness of our framework, we formulate the likelihood function of the correlations of 2D image matches as a Gaussian and Uniform mixture distribution which takes the uncertainty caused by illumination changes, image noise and moving objects into account.
PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction
Though nowadays unsupervised learning methods have been proposed and have gotten gratifying results, those methods still fail to reconstruct intact results in challenging scenes, such as weakly-textured surfaces, as those methods primarily depend on pixel-wise photometric consistency which is subjected to various illuminations.
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems.
DDR-Net: Learning Multi-Stage Multi-View Stereo With Dynamic Depth Range
Distinct from these approaches, we propose a Dynamic Depth Range Network (DDR-Net) to determine the depth range hypotheses dynamically by applying a range estimation module (REM) to learn the uncertainties of range hypotheses in the former stages.
Deformable spatial propagation network for depth completion
To tackle this issue, in this paper, we propose a deformable spatial propagation network (DSPN) to adaptively generates different receptive field and affinity matrix for each pixel.
Vanishing Point Guided Natural Image Stitching
Recently, works on improving the naturalness of stitching images gain more and more extensive attention.
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images.
Coarse-to-fine Optimization for Speech Enhancement
However, due to the large variance of the enhanced speech with even the same cosine similarity loss in high dimensional space, a deep neural network learnt with this loss might not be able to predict enhanced speech with good quality.
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method
We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus.
Multiple Combined Constraints for Image Stitching
In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary.
Generalized Content-Preserving Warps for Image Stitching
Content-Preserving Warping (CPW) is a typical method to deal with this issue, in which geometric and photometric constraints are imposed to guide the warping process.
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset
First, we demonstrate that video attractiveness and different engagements present different relationships.
Regularizing RNNs for Caption Generation by Reconstructing The Past with The Present
Recently, caption generation with an encoder-decoder framework has been extensively studied and applied in different domains, such as image captioning, code captioning, and so on.
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network.
Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network
We propose a novel Cascaded Convolutional Text Network (CCTN) that joints two customized convolutional networks for coarse-to-fine text localization.
Text-attentional convolutional neural network for scene text detection
Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images.
Text-Attentional Convolutional Neural Networks for Scene Text Detection
The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components.
Line-Based Multi-Label Energy Optimization for Fisheye Image Rectification and Calibration
Fisheye image rectification and estimation of intrinsic parameters for real scenes have been addressed in the literature by using line information on the distorted images.
Real-Time Coarse-to-Fine Topologically Preserving Segmentation
In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels.
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs
Recent trends in semantic image segmentation have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning.
