Search Results for author:
Found 14 papers, 4 papers with code
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors.
Reassessing the Limitations of CNN Methods for Camera Pose Regression
In this paper, we address the problem of camera pose estimation in outdoor and indoor scenarios.
HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss
Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature.
D2D: Keypoint Extraction with Describe to Detect Approach
In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations.
SOLAR: Second-Order Loss and Attention for Image Retrieval
One is focused on second-order spatial information to increase the performance of image descriptors, both local and global.
UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision
In this paper, we explore how three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework, which is trained without the need of training data with point to point correspondences.
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors.
RelocNet: Continuous Metric Learning Relocalisation using Neural Nets
We propose a method of learning suitable convolutional representations for camera pose retrieval based on nearest neighbour matching and continuous metric learning-based feature descriptors.
Pose Guided RGBD Feature Learning for 3D Object Pose Estimation
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation.
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
In this work, we investigate several methods and strategies to learn deep embeddings for face recognition, using joint sample- and set-based optimization.
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
In this paper, we propose a novel benchmark for evaluating local image descriptors.
Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art.
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors
We address this problem and propose a CNN based descriptor with improved matching performance, significantly reduced training and execution time, as well as low dimensionality.
BOLD - Binary Online Learned Descriptor For Efficient Image Matching
The patch adapted descriptors are then efficiently built online from a subset of tests which lead to lower intra class distances thus a more robust descriptor.
