**Region-based Fully Convolutional Networks**, or **R-FCNs**, are a type of region-based object detector. In contrast to previous region-based object detectors such as Fast/[Faster R-CNN](https://paperswithcode.com/method/faster-r-cnn) that apply a costly per-region subnetwork hundreds of times, R-FCN is fully convolutional with almost all computation shared on the entire image.

To achieve this, R-FCN utilises position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.

A Mixer layer is a layer used in the MLP-Mixer architecture proposed by Tolstikhin et. al (2021) for computer vision. Mixer layers consist purely of MLPs, without convolutions or attention. It takes an input of embedded image patches (tokens), with its output having the same shape as its input, similar to that of a Vision Transformer encoder. As suggested by its name, Mixer layers "mix" tokens and channels through its "token mixing" and "channel mixing" MLPs contained the layer. It utilizes previous techniques by other architectures, such as layer normalization, skip-connections, and regularization methods.

Image credit: Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., ... & Dosovitskiy, A. (2021). Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems, 34, 24261-24272.

Mixer Layer

MLP-Mixer: An all-MLP Architecture for Vision

R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

In this work, we present a conceptually simple and effective method to train a strong bilingual multimodal representation model. Starting from the pretrained multimodal representation model CLIP released by OpenAI, we switched its text encoder with a pretrained multilingual text encoder XLM-R, and aligned both languages and image representations by a two-stage training schema consisting of teacher learning and contrastive learning. We validate our method through evaluations of a wide range of tasks. We set new state-of-the-art performances on a bunch of tasks including ImageNet-CN, Flicker30k- CN, and COCO-CN. Further, we obtain very close performances with CLIP on almost all tasks, suggesting that one can simply alter the text encoder in CLIP for extended capabilities such as multilingual understanding. Our models and code are available at https://github.com/FlagAI-Open/FlagAI.

Source	R-FCN: Object Detection via Region-based Fully Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com