Tag: mask-rcnn

• DiffusionDet Diffusion Model for Object Detection (06 Oct 2023)
  

  This is my reading note for DiffusionDet: Diffusion Model for Object Detection. This paper formulates the object detection problem as a diffusion process: recover object bounding box from noisy estimation. The initial estimation could be from purely random Gaussian noise. One benefit of this method is that it could automatically handle different number of bounding boxes

• MDETR -Modulated Detection for End-to-End Multi-Modal Understanding (16 Jul 2023)
  

  This is my reading note for MDETR -Modulated Detection for End-to-End Multi-Modal Understanding. This paper proposes a method to learn object detection model from pairs of image and tree form text. The trained model is found to be capable of localizing unseen / long tail category.

• NeuMan Neural Human Radiance Field from a Single Video (26 Sep 2022)
  

  NeuMan: Neural Human Radiance Field from a Single Video proposes a novel framework to reconstruct the human and the scene that can be ren- dered with novel human poses and views from just a single in-the-wild video. Given a video captured by a moving camera, we train two NeRF models: a human NeRF model (condition on SMPL) and a scene NeRF model. Our method is able to learn subject specific details, including cloth wrinkles and ac- cessories, from just a 10 seconds video clip, and to provide high quality renderings of the human under novel poses, from novel views, together with the background.

• Object Detections (02 Apr 2019)
  

  Here is the comparison of the most popular object detection frameworks.

• Image Segmentation (02 Apr 2019)
  

  There are two types of image segmentation:

  • semantic segmentation: assign labels to each pixel
  • instance segmentation: extract the bounary/mask for each instance in the image