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Found 6 papers, 0 papers with code
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.
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.
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.
