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Found 12 papers, 4 papers with code
Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models
To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151, 380 image patches.
On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids.
A New Dataset and Comparative Study for Aphid Cluster Detection
Aphids are one of the main threats to crops, rural families, and global food security.
Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets
After carefully examining the self-attention modules, we discover that the number of trivial attention weights is far greater than the important ones and the accumulated trivial weights are dominating the attention in Vision Transformers due to their large quantity, which is not handled by the attention itself.
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients
Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks.
Dilated Continuous Random Field for Semantic Segmentation
Mean field approximation methodology has laid the foundation of modern Continuous Random Field (CRF) based solutions for the refinement of semantic segmentation.
SGNet: A Super-class Guided Network for Image Classification and Object Detection
A super-class branch (SCB), trained on super-class labels, is introduced to guide finer class prediction.
Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
This work can serve as a baseline for future research in polyp detection and classification.
Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling
This paper, for the first time, reveals the fundamental reason that impedes the scale-up of layer-wise learning is due to the relatively poor separability of the feature space in shallow layers.
Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection
In this paper, we aim at single-shot object detectors and propose a location-aware anchor-based reasoning (LAAR) for the bounding boxes.
A Comparative Study on Polyp Classification using Convolutional Neural Networks
In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification.
Object Detection with Convolutional Neural Networks
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN).
