**RFB Net** is a one-stage object detector that utilises a receptive field block module. It utilises a VGG16 backbone, and is otherwise quite similar to the [SSD](https://paperswithcode.com/method/ssd) architecture.

https://developer.nvidia.com/blog/flip-a-difference-evaluator-for-alternating-images/

FLIP

FLIP: A Difference Evaluator for Alternating Images

RFB Net

Receptive Field Block Net for Accurate and Fast Object Detection

**Bi3D** is a stereo depth estimation framework that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth *D*, as existing stereo methods do, it classifies them as being closer or farther than *D*. It takes the stereo pair and a disparity $d\_{i}$ and produces a confidence map, which can be thresholded to yield the binary segmentation. To estimate depth on $N + 1$ quantization levels we run this network $N$ times and maximize the probability in Equation 8 (see paper). To estimate continuous depth, whether full or selective, we run the [SegNet](https://paperswithcode.com/method/segnet) block of Bi3DNet for each disparity level and work directly on the confidence volume.

Source	Receptive Field Block Net for Accurate and Fast Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com