GZU-SAMLab

AA-Trans

AA-trans: Core attention aggregating transformer with informationentropy selector for fine-grained visual classification

CDKM

Common and Distinct Knowledge Mining Network with Content Interaction for Dense Captioning

CSNet

Count-Supervised Network (CSNet) can complete the counting of wheat ears with only quantitative supervision. CSNet: A Count-supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting

DRC

LCM-Captioner

LCM-Captioner is an efficient model for Text-based Image Captioning(TextCap).

McQNet

Meta-contrastive Learning with Support-based Query Interaction for Few-shot Fine-grained Visual Classification

MISL

We propose a text-guided image inpainting method with multi-grained image-text semantic learning (MISL), consisting of global-local generators and discriminators.

MSKD

Mutil-stage knowledge distillation (MSKD) can facilitate the accuracy of plant disease detection, which may be a new and vital direction for lightweight algorithmic models in smart agriculture with practical applications.

PlanText

PlanText: Gradually Masked Guidance to Align Image Phenotype with Trait Description for Plant Disease Texts Tool

VegSegment_SR

Phenotype segmentation method based on spectral reconstruction for UAV field vegetation.

GZU-SAMLab/AA-Trans

AA-trans: Core attention aggregating transformer with informationentropy selector for fine-grained visual classification

GZU-SAMLab/McQNet

Meta-contrastive Learning with Support-based Query Interaction for Few-shot Fine-grained Visual Classification

GZU-SAMLab/CDKM

Common and Distinct Knowledge Mining Network with Content Interaction for Dense Captioning

GZU-SAMLab/MISL

We propose a text-guided image inpainting method with multi-grained image-text semantic learning (MISL), consisting of global-local generators and discriminators.

GZU-SAMLab/MSKD

Mutil-stage knowledge distillation (MSKD) can facilitate the accuracy of plant disease detection, which may be a new and vital direction for lightweight algorithmic models in smart agriculture with practical applications.

GZU-SAMLab/CSNet

Count-Supervised Network (CSNet) can complete the counting of wheat ears with only quantitative supervision. CSNet: A Count-supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting

GZU-SAMLab/LCM-Captioner

LCM-Captioner is an efficient model for Text-based Image Captioning(TextCap).

GZU-SAMLab/VegSegment_SR

Phenotype segmentation method based on spectral reconstruction for UAV field vegetation.

GZU-SAMLab/PlanText

PlanText: Gradually Masked Guidance to Align Image Phenotype with Trait Description for Plant Disease Texts Tool

GZU-SAMLab/DRC

GZU-SAMLab/EMGE

This is EMGE

GZU-SAMLab/HLNet

HLNet: Hybrid Learning Network via Implicit Relationship Construction for Spatial-Spectral Super-resolution