TWEC is a method to generate temporal word embeddings: this method is efficient and it is based on a simple heuristic: we train an atemporal word embedding, the compass and we use this embedding to freeze one of the layers of the CBOW architecture. The frozen architecture is then used to train time-specific slices that are all comparable after training.

**Segmentation Transformer**, or **SETR**, is a [Transformer](https://paperswithcode.com/methods/category/transformers)-based segmentation model. The transformer-alone encoder treats an input image as a sequence of image patches represented by learned patch embedding, and transforms the sequence with global self-attention modeling for discriminative feature representation learning. Concretely, we first decompose an image into a grid of fixed-sized patches, forming a sequence of patches. With a linear embedding layer applied to the flattened pixel vectors of every patch, we then obtain a sequence of feature embedding vectors as the input to a transformer. Given the learned features from the encoder
transformer, a decoder is then used to recover the original image resolution. Crucially there is no downsampling in spatial resolution but global context modeling at every layer of the encoder transformer.

SETR

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

TWEC

Training Temporal Word Embeddings with a Compass

CondInst is a simple yet effective instance segmentation framework. It eliminates ROI cropping and feature alignment with the instance-aware mask heads. As a result, CondInst can solve instance segmentation with fully convolutional networks. CondInst is able to produce high-resolution instance masks without longer computational time. Extensive experiments show that CondInst can achieve even better performance and inference speed than [Mask R-CNN](https://paperswithcode.com/method/mask-r-cnn). It can be a strong alternative to previous ROI-based instance segmentation methods. Code is at https://github.com/aim-uofa/AdelaiDet.

Source	Training Temporal Word Embeddings with a Compass
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com