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Found 5 papers, 2 papers with code
CTS: A Consistency-Based Medical Image Segmentation Model
Compared to diffusion models, consistency models can reduce the sampling times to once, not only achieving similar generative effects but also significantly speeding up training and prediction.
MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion
Multi-modality image fusion (MMIF) aims to integrate complementary information from different modalities into a single fused image to represent the imaging scene and facilitate downstream visual tasks comprehensively.
Diving with Penguins: Detecting Penguins and their Prey in Animal-borne Underwater Videos via Deep Learning
In this paper, we publish an animal-borne underwater video dataset of penguins and introduce a ready-to-deploy deep learning system capable of robustly detecting penguins (mAP50@98. 0%) and also instances of fish (mAP50@73. 3%).
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering
Since most current medical VQA models focus on visual content, ignoring the importance of text, this paper proposes a multi-view attention-based model(MuVAM) for medical visual question answering which integrates the high-level semantics of medical images on the basis of text description.
