Scientific Research Publishing

Geo-Refined Point Transformer: Coordinate-Aware Excitation and Positional Upsampling for 3D Scene Segmentation ()

The proposed Coordinate-Aware Feature Excitation (CAFE) module and Position-Aware Upsampling (Pos-Up) module both adhere to ...

New Apple model combines vision understanding and image generation with impressive results

Manzano combines visual understanding and text-to-image generation, while significantly reducing performance or quality trade-offs.

GLM-Image explained: Huawei-powered AI that seriously challenges Nvidia, here’s how

For the past few years, a single axiom has ruled the generative AI industry: if you want to build a state-of-the-art model, ...
Frontiers

A combined approach to lithology identification using reinforcement learning and transformer algorithms

Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification ...
IEEE

Evaluation of Encoder-Only Transformer for Multi-Step Traffic Flow Prediction

Abstract: Traffic flow prediction is critical for Intelligent Transportation Systems to alleviate congestion and optimize traffic management. The existing basic Encoder-Decoder Transformer model for ...
Frontiers

CoastVisionNet: transformer with integrated spatial-channel attention for coastal land cover classification

1 School of Integrated Circuits, Guangdong University of Technology, Guangzhou, China 2 School of Computer Science, Xi'an University of Technology, Xi'an, China Introduction: The rapid advancement of ...
IEEE

Medical Report Generation With Knowledge Distillation and Multi-Stage Hierarchical Attention in Vision Transformer Encoder and GPT-2 Decoder

Abstract: Automated medical report generation is a challenging task that involves synthesizing diagnostic findings and clinical observations from medical images. In this study, we propose a novel ...
GitHub

Understanding Self-Attention(Encoder's Self-Attention and Decoder's Masked Self-Attention) in Transformers

- Driven by the **output**, attending to the **input**. - Each word in the output sequence determines which parts of the input sequence to attend to, forming an **output-oriented attention** mechanism ...