Public articles linked to the same research event.
arXiv The work proposes AGGRNet, which embeds a Feature Extraction and Aggregation (FEA) module and a C2PCA block into a YOLOv11 classification backbone; spatial and channel attention plus a learnable threshold τ split feature maps into informative and non-informative parts that are then aggregated by cross-attention, yielding results above the compared SOTA models on five public datasets, including a 5.48% accuracy gain on Kvasir and a 2.2% gain on LIMUC.
The work proposes AGGRNet, which embeds a Feature Extraction and Aggregation (FEA) module and a C2PCA block into a YOLOv11 classification backbone; spatial and channel attention plus a learnable threshold τ split feature maps into informative and non-informative parts that are then aggregated by cross-attention, yielding results above the compared SOTA models on five public datasets, including a 5.48% accuracy gain on Kvasir and a 2.2% gain on LIMUC.
The work proposes AGGRNet, which embeds a Feature Extraction and Aggregation (FEA) module and a C2PCA block into a YOLOv11 classification backbone; spatial and channel attention plus a learnable threshold τ split feature maps into informative and non-informative parts that are then aggregated by cross-attention, yielding results above the compared SOTA models on five public datasets, including a 5.48% accuracy gain on Kvasir and a 2.2% gain on LIMUC.
The work proposes AGGRNet, which embeds a Feature Extraction and Aggregation (FEA) module and a C2PCA block into a YOLOv11 classification backbone; spatial and channel attention plus a learnable threshold τ split feature maps into informative and non-informative parts that are then aggregated by cross-attention, yielding results above the compared SOTA models on five public datasets, including a 5.48% accuracy gain on Kvasir and a 2.2% gain on LIMUC.