Feature Pyramid Network Feature pyramids are a basic component for detecting objects on different scales. Before this paper, a lot of research has been avoiding these pyramid structures due to their high computational and memory costs. Feature Pyramid Network tackles…
ArcFace Loss One of tha main challenges in feature learning using Deep Convolutional Neural Networks for large scale face recognition is designing the optimal loss function that enhance discriminative power. Before this paper, there were mainly 2 lines to train…
Deep Metric Learning Let’s say we want to create a model that can do face recognition(Face Identification and face verification). We CAN use traditional deep learning since it can perform really well but it requires a lot of training data.…
SILU SiLU is proposed as an activation function for neural network function approximation in reinforcement learning, and DSiLU is the derivative function for SiLU. DSiLU is a steeper and “overshot” version of the sigmoid function and it is proposed as…
Rubik’s Cube Due to the annotation of 3D medical data being hard to acquire, the number of annotated 3D images for training is often not enough. Self-supervised learning deeply exploiting the information of raw data can be a solution to…
PVTv2 The previous PVT had mainly 3 limitations. When processing high-resolution images, the computational cost is still relatively high. Loses local continuity of the image because it treats the image as a sequence of non-overlapping patches. Inflexible for arbitrary image…
DETR Modern object detectors predict a set of bounding boxes and category labels for each object of interest by defining surrogate regression and classification problems on a large set of proposals. This means that their performances heavily rely on post-processing…
Background When using Traditional CNN-backboned architecture models, due to the convolutional filter’s weights being fully fixed after training, they suffered to adapt to different inputs dynamically. Vision Transformers attempted to remove the convolution from the backbone, but since it is…
Polynomial Learning Rate For deep learning models, the learning rate is one of the most important hyper-parameters in any deep neural network optimization process. Polynomial Learning Rate is a proposed technique to apply learning rate decay and optimize such process.…
PIDNet Today I’ve learned about PIDNet, so I’d like to share it here. Previously, I learned about BiSeNet which had a two-branched architecture to solve high latency problems. However, this architecture suffers another problem called “overshoot” where the boundary of…