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Found 13 papers, 5 papers with code
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application
Inference & Application","We formalize concepts around geometric occlusion in 2D images (i. e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation.
Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application
The former provides a way to generate large-scale accurate occlusion datasets while, based on the latter, we propose a novel method for task-independent pixel-level occlusion relationship estimation from single images.
Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-Net [1] and triplet based metric learning [2].
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples.
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
In the proposed networks, different levels of features at each spatial location are adaptively re-weighted according to the local structure and surrounding contextual information before aggregation.
Deep Ordinal Regression Network for Monocular Depth Estimation
These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions.
Ranked #13 on
Depth Estimation
on NYU-Depth V2
A Compromise Principle in Deep Monocular Depth Estimation
However, we find that training a network to predict a high spatial resolution continuous depth map often suffers from poor local solutions.
Perceptual Adversarial Networks for Image-to-Image Transformation
The proposed PAN consists of two feed-forward convolutional neural networks (CNNs), the image transformation network T and the discriminative network D. Through combining the generative adversarial loss and the proposed perceptual adversarial loss, these two networks can be trained alternately to solve image-to-image transformation tasks.
Tag Disentangled Generative Adversarial Networks for Object ImageRe-rendering
The whole framework consists of a disentangling network, a generative network, a tag mapping net, and a discriminative network, which are trained jointly based on a given set of images that are complete/partially tagged(i. e., supervised/semi-supervised setting).
Occlusion Boundary Detection via Deep Exploration of Context
Occlusion boundaries contain rich perceptual information about the underlying scene structure.
Scene-Domain Active Part Models for Object Representation
In this paper, we are interested in enhancing the expressivity and robustness of part-based models for object representation, in the common scenario where the training data are based on 2D images.
MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking
Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking.
Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques
This paper introduces an efficient approach to integrating non-local statistics into the higher-order Markov Random Fields (MRFs) framework.
