Found 30 repositories(showing 30)
rayleizhu
[CVPR 2023] Official code release of our paper "BiFormer: Vision Transformer with Bi-Level Routing Attention"
mkang315
[MICCAI'24] Official implementation of "BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection".
JunHeum
BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation, CVPR2023
ushareng
BiFormer: Vision Transformer with Bi-Level Routing Attention
Westbrone
Try to use the more lightweight BiFormer's attention mechanism combined with the UNet network to build a Bi-unet medical segmentation model.
ZhangIceNight
Official PyTorch Implementation of "Hypergraph BiFormer" (TGRS 2025)
akianfar
Code for paper "Deep-CBN: Integrating Convolutional Layers and Biformer Network with Forward-Forward and Backpropagation Training "
jiadai2
xiaomubiaojiance 利用BiFormer注意力机制改进YOLOv8检测模型用于小目标检测
shengyexu
No description available
Westbrone
Biformer_main_network_by_chinese_explanation
ZhangIceNight
[CVPR 2023] Official code release of our paper "BiFormer: Vision Transformer with Bi-Level Routing Attention"
Jacky-Android
No description available
williamgyy
No description available
guixuef
No description available
wlc2424762917
from rayleizhu
whdcode
No description available
QiZhang3709
No description available
xiyuweifeng267
No description available
LiaoYun0x0
No description available
Devil-hash-ai
No description available
singhkshubh
No description available
FengChangQun
None
FZU-N
[TMM'2024] BiFormer: Bilateral Interaction for Local-Global Collaborative Perception in Low-Light Image Enhancement
XDpaddle
No description available
Mufsh
No description available
RayTan183
No description available
yangchenxi000
No description available
nishantrs0404
Here we will develop a model using BiFormer-YOLOv8s to identify the crops and there disease.
YongChaoLiang
Supplementary material to the article “Pest recognition with higher accuracy and lower parameters for large-scale datasets based on Biformer”
harshitaji
🔧 Hybrid YOLO Architecture Update Integrated BiFormer, CBAM, and a simplified DETR (encoder-only) into the YOLO backbone for efficient feature refinement. Enhances local-global context, spatial-channel focus, and object-level relations to boost defect detection accuracy.
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