Search Results for author:
Found 11 papers, 3 papers with code
A Study on the Efficient Product Search Service for the Damaged Image Information
With the development of Information and Communication Technologies and the dissemination of smartphones, especially now that image search is possible through the internet, e-commerce markets are more activating purchasing services for a wide variety of products.
Multi-level Distance Regularization for Deep Metric Learning
These allow the parameters of the embedding network to be settle on a local optima with better generalization.
Suppressing Spoof-irrelevant Factors for Domain-agnostic Face Anti-spoofing
In the second adversarial learning scheme, each of the discrimination heads is also adversarially trained to suppress a spoof factor, and the group of the secondary spoof classifier and the encoder aims to intensify the spoof factor by overcoming the suppression.
BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition
Moreover, we propose a novel compensation method to increase the number of referenced instances in the training stage.
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch.
Detector With Focus: Normalizing Gradient In Image Pyramid
An image pyramid can extend many object detection algorithms to solve detection on multiple scales.
Attentional Feature-Pair Relation Networks for Accurate Face Recognition
In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores.
Pairwise Relational Networks using Local Appearance Features for Face Recognition
We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and captures unique pairwise relations with the same identity and discriminative pairwise relations between different identities.
BAN: Focusing on Boundary Context for Object Detection
To verify our method, we visualize the activation of the sub-networks according to the boundary contexts and empirically show that the sub-networks contribute more to the related sub-problem in detection.
Pairwise Relational Networks for Face Recognition
Because the existence and meaning of pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance patches on the feature maps, to the PRN.
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
However, due to the lack of scale normalization in CNN-based detection methods, the activated channels in the feature space can be completely different according to a scale and this difference makes it hard for the classifier to learn samples.
