This project explores the impact of Multi-Scale CNNs on the classification of EEG signals in Brain-Computer Interface (BCI) systems. By comparing the performance of two models, EEGNet and MSTANN, the study demonstrates how richer temporal feature extractions can enhance CNN models in classifying EEG signals

A lightweight multi-scale channel attention network for image super-resolution

An image classification neural network using multi scale features.

Multi-Modal scene recognition using multi-scale encoded features (IMAVIS 2022)

Official implementation of "Exploiting Scale-Variant Attention for Segmenting Small Medical Objects"

The code of AGCN (Attention-driven Graph Clustering Network), which is accepted by ACM MM 2021.

Nested U-Net with two-level skip connections for speech enhancement

[ICCV 2021] Official implementation of the paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"

CVPR2020, Multi-scale Interactive Network for Salient Object Detection

Official implementation of CrossViT. https://arxiv.org/abs/2103.14899