A **Bidirectional LSTM**, or **biLSTM**, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards direction. BiLSTMs effectively increase the amount of information available to the network, improving the context available to the algorithm (e.g. knowing what words immediately follow *and* precede a word in a sentence).

Image Source: Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks, Cornegruta et al

**Adaptive Non-Maximum Suppression** is a non-maximum suppression algorithm that applies a dynamic suppression threshold to an instance according to the target density. The motivation is to find an NMS algorithm that works well for pedestrian detection in a crowd. Intuitively, a high NMS threshold keeps more crowded instances while a low NMS threshold wipes out more false positives. The adaptive-NMS thus applies a dynamic suppression strategy, where the threshold rises as instances gather and occlude each other and decays when instances appear separately. To this end, an auxiliary and learnable sub-network is designed to predict the adaptive NMS threshold for each instance.

Adaptive NMS

Adaptive NMS: Refining Pedestrian Detection in a Crowd

BiLSTM

**Darknet-53** is a convolutional neural network that acts as a backbone for the [YOLOv3](https://paperswithcode.com/method/yolov3) object detection approach. The improvements upon its predecessor [Darknet-19](https://paperswithcode.com/method/darknet-19) include the use of residual connections, as well as more layers.

Year	2001
Data Source	CC BY-SA - https://paperswithcode.com