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Found 5 papers, 3 papers with code
FHIST: A Benchmark for Few-shot Classification of Histological Images
We build few-shot tasks and base-training data with various tissue types, different levels of domain shifts stemming from various cancer sites, and different class-granularity levels, thereby reflecting realistic scenarios.
3D Fully Convolutional Neural Networks with Intersection Over Union Loss for Crop Mapping from Multi-Temporal Satellite Images
We conclude that using the IOU loss function provides a superior choice to learn individual crop types.
DFNet: Discriminative feature extraction and integration network for salient object detection
To address the second challenge, we propose an Attention-based Multi-level Integrator Module to give the model the ability to assign different weights to multi-level feature maps.
Ensembles of Deep Neural Networks for Action Recognition in Still Images
A big challenge in action recognition in still images is the lack of large enough datasets, which is problematic for training deep Convolutional Neural Networks (CNNs) due to the overfitting issue.
CAGNet: Content-Aware Guidance for Salient Object Detection
Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results.
