**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.

**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.

CornerNet-Saccade

CornerNet-Lite: Efficient Keypoint Based Object Detection

TridentNet

Scale-Aware Trident Networks for Object Detection

BCI MI signal Classification Framework using Fuzzy integrals.

Paper: Ko, L. W., Lu, Y. C., Bustince, H., Chang, Y. C., Chang, Y., Ferandez, J., ... & Lin, C. T. (2019). Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. IEEE Computational Intelligence Magazine, 14(1), 96-106.

Source	Scale-Aware Trident Networks for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com