What It Does One-Shot learning can be useful when you want to identify someone just by giving the predicting model a single picture of them. Similarity Function A model can perform One-shot learning by learning a “similarity” function. When the…
Abstract Point Pillar is an architecture proposed for 3D object detection using point clouds as inputs. Architecture The architecture consists of mainly 3 elements. Pillar Feature Net BackBone Detection Head 1. Pillar Feature Net This phase takes the following steps.…
Depth Estimation The main challenges in monocular 3D object detection lie in accurately predicting object depth. CaDDN(Categorical Depth Distribution Network) uses a predicted categorical depth distribution for each pixel to project appropriate depth in 3D space. Approaches There are several…
Types Here are some approaches to measure depth by only using RGB images from motion. Optical Expansion Observe how the length of an object changes as the camera moves closer. If the object is close, the length will dramatically change…
Compared with 2D Object Detection, there are several additional parameters to consider when it comes to 3D object detection 2D Object Detection: X coordinate for the center of the bounding box Y coordinate for the center of the bounding box…
Non-Max Suppression is a post-processing method for object detection tasks. In most cases, an object detection model will predict multiple boxes for a single object like the picture in my note. However, we don’t want this crowded output. We instead…
Selective Search Selective search is a region proposal method for object detection. It hierarchically groups similar regions based on color, texture, size, and shape. Selective Search uses over-segmented images as input. Then takes the following steps. 1. Add all bounding…
The traditional way to detect cracks inside concretes is to use A) Radar data of the section of the concrete and B) Label Image of the section to train a pix2pix model. But, this methodology struggles to detect depending on…
This is how an AI learns how to classify my dog. I’ve found a blog post visualizing the feature maps of a classification model, so I tried it out! I’m going to use Resnet18 and visualize the feature maps for…
I’ve experimented how standardization affect a binary segmentation model’s performance by comparing 2 outputs. Inference result from model trained with standardization using ImageNet RGB mean and standard deviation Inference result from model trained without standardization I’ve found out that the…