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Classification and detection of insects from field images using deep learning for smart pest management: A systematic review
W Li, T Zheng, Z Yang, M Li, C Sun, X Yang - Ecological Informatics, 2021 - Elsevier
Insect pest is one of the main causes affecting agricultural crop yield and quality all over the
world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction …
world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction …
Insect pest image detection and recognition based on bio-inspired methods
Insect pests recognition is necessary for crop protection in many areas of the world. In this
paper we propose an automatic classifier based on the fusion between saliency methods …
paper we propose an automatic classifier based on the fusion between saliency methods …
Texture-guided saliency distilling for unsupervised salient object detection
Abstract Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies
on the noisy saliency pseudo labels that have been generated from traditional handcraft …
on the noisy saliency pseudo labels that have been generated from traditional handcraft …
CAGNet: Content-aware guidance for salient object detection
S Mohammadi, M Noori, A Bahri, SG Majelan… - Pattern Recognition, 2020 - Elsevier
Abstract Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection
methods have achieved promising results. However, it is still challenging to learn effective …
methods have achieved promising results. However, it is still challenging to learn effective …
[HTML][HTML] Re-abstraction and perturbing support pair network for few-shot fine-grained image classification
The goal of few-shot fine-grained image classification (FSFGIC) is to distinguish subordinate-
level categories with subtle visual differences such as the species of bird and models of car …
level categories with subtle visual differences such as the species of bird and models of car …
A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images
Large variations in anatomical shape and size, too much overlap between anatomical
structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x …
structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x …
Causal interventional training for image recognition
Deep learning models often fit undesired dataset bias in training. In this paper, we formulate
the bias using causal inference, which helps us uncover the ever-elusive causalities among …
the bias using causal inference, which helps us uncover the ever-elusive causalities among …
Deep object co-segmentation and co-saliency detection via high-order spatial-semantic network modulation
Object co-segmentation (CSG) is to segment the common objects of the same category in
multiple relevant images while the co-saliency detection (CSD) aims to discover the salient …
multiple relevant images while the co-saliency detection (CSD) aims to discover the salient …
Deep object co-segmentation via spatial-semantic network modulation
Object co-segmentation is to segment the shared objects in multiple relevant images, which
has numerous applications in computer vision. This paper presents a spatial and semantic …
has numerous applications in computer vision. This paper presents a spatial and semantic …
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context
Unsupervised salient object detection aims to detect salient objects without using
supervision signals eliminating the tedious task of manually labeling salient objects. To …
supervision signals eliminating the tedious task of manually labeling salient objects. To …