**CornerNet-Saccade** is an extension of [CornerNet](https://paperswithcode.com/method/cornernet) with an attention mechanism similar to saccades in human vision. It starts with a downsized full image and generates an attention map, which is then zoomed in on and processed further by the model. This differs from the original CornerNet in that it is applied fully convolutionally across multiple scales.

**Off-Diagonal Orthogonal Regularization** is a modified form of [orthogonal regularization](https://paperswithcode.com/method/orthogonal-regularization) originally used in [BigGAN](https://paperswithcode.com/method/biggan). The original orthogonal regularization is known to be limiting so the authors explore several variants designed to relax the constraint while still imparting the desired smoothness to the models. They opt for a modification where they remove diagonal terms from the regularization, and aim to minimize the pairwise cosine similarity between filters but does not constrain their norm:

$$ R\_{\beta}\left(W\right) = \beta|| W^{T}W \odot \left(\mathbf{1}-I\right) ||^{2}\_{F} $$

where $\mathbf{1}$ denotes a matrix with all elements set to 1. The authors sweep $\beta$ values and select $10^{−4}$.

Off-Diagonal Orthogonal Regularization

Large Scale GAN Training for High Fidelity Natural Image Synthesis

CornerNet-Saccade

CornerNet-Lite: Efficient Keypoint Based Object Detection

**TridentNet** is an object detection architecture that aims to generate scale-specific feature
maps with a uniform representational power.  A parallel multi-branch architecture is constructed in which each branch shares the same transformation parameters but with different receptive fields. A scale-aware training scheme is used to specialize each branch by sampling object instances of proper scales for training.

Source	CornerNet-Lite: Efficient Keypoint Based Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com