We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are first combined with a set of learned position biases, the result of which is multiplied with the query in an el...
The attention function used by the transformer takes three inputs: Q,K,V. The equation used to calculate the attention weights, which is scaled by a factor of square root of the depth is:Multi-head attentionMulti-head attention consists of four parts: Linear layers、Multi-head attention、...
Ultimate-Awesome-Transformer-Attention This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites. This list is maintained by Min-Hung Chen. (Actively keep updating) If you find some ignored papers, feel free to create pull re...
We then employ attention and transformer modules to model contextual information in bi-temporal images effectively. Additionally, we use feature exchange to bridge the domain gap between different temporal image domains by partially exchanging features between the two Siamese branches of our AMTNet. ...
The transformer structure has gained attention in computer vision (CV) tasks such as image classification, semantic segmentation, and object detection. It efficiently models global contextual information through an encoder–decoder architecture compared to pure ConvNets. This success has motivated the devel...
几篇论文实现代码:《LiDAR R-CNN: An Efficient and Universal 3D Object Detector》(CVPR 2021) GitHub:https:// github.com/TuSimple/LiDAR_RCNN [fig1] 《PCLs: Geometry-aware Neural Reconstruction of 3D...
First, we use the R-Transformer model to obtain the global and local information of the text sequence in combination with part-of-speech embedding. At the same time, we use BiLSTM+CRF to obtain the entity information of the text, and use the self-attention mechanism to obtain the keywords...
第1章 TextMonkey : An OCR-Free Large Multimodal Model for Understanding Document 摘要 1 INTRODUCTION 2 RELATED WORKS 2.1 OCR-Model-Driven Methods 2.2 OCR-Free Methods 3 METHODOLOGY 3.1 Shifted Window Attention 3.2 Token Resampler 3.3 Position-Related Task 3.4 Dataset Construction 3....
Transformer-Dynet (https://github.com/duyvuleo/Transformer-DyNet) - Baseline 1 (small model) (2 heads, 2 encoder/decoder layers, sinusoid positional encoding, 128 units, SGD, beam5) w/ dropout (0.1) (source and target embeddings, sub-layers (attention + feedforward)) and label smoothing ...
An implementation of Performer, a linear attention-based transformer, in Pytorch - lucidrains/performer-pytorch