**PAFNet** is an anchor-free detector for object detection that removes pre-defined anchors and regresses the locations directly, which can achieve higher efficiency. The overall network is composed of a backbone, an up-sampling module, an AGS module, a localization branch and a regression branch. Specifically,  ResNet50-vd is chosen as the backbone for server side, and [MobileNetV3](https://paperswithcode.com/method/mobilenetv3) for mobile side. Besides, for mobile side, we replace traditional [convolution](https://paperswithcode.com/method/convolution) layers with lite convolution operators.

The **Enhanced Fusion Framework** proposes three different ideas to improve the existing MI-based BCI frameworks.

Image source: [Fumanal-Idocin et al.](https://arxiv.org/pdf/2101.06968v1.pdf)

Enhanced Fusion Framework

Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

PAFNet

PAFNet: An Efficient Anchor-Free Object Detector Guidance

**context2vec** is an unsupervised model for learning generic context embedding of wide sentential contexts, using a bidirectional [LSTM](https://paperswithcode.com/method/lstm). A large plain text corpora is trained on to learn a neural model that embeds entire sentential contexts and target words in the same low-dimensional space, which
is optimized to reflect inter-dependencies between targets and their entire sentential context as a whole. 

In contrast to word2vec that use context modeling mostly internally and considers the target word embeddings as their main output, the focus of context2vec is the context representation. context2vec achieves its objective by assigning similar embeddings to sentential contexts and their associated target words.

Source	PAFNet: An Efficient Anchor-Free Object Detector Guidance
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com