Today, we are going to discuss a [method](https://arxiv.org/abs/2011.12450?ref=hackernoon.com) proposed by researchers from four institutions one of which is ByteDance AI Lab (known for their TikTok App). They give us a new method termed **Sparse...
Today, we are going to discuss a method proposed by researchers from four institutions one of which is ByteDance AI Lab (known for their TikTok App). They give us a new method termed Sparse R-CNN (not confuse with Sparse R-CNN that works with sparse convolutions on 3D computer vision tasks such that) that achieve near state-of-the-art performance in object detection and uses completely sparse and learnable bounding boxes generation.
Let’s start with a short overview of existing detecting methods.
One of the widely used pipelines is the one-stage detector, which directly predicts the label and location of anchor boxes densely covering spatial positions, scales, and aspect ratios in a single-shot way. For example SSD or YOLO.
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