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Found 9 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.
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.
Explicitly Increasing Input Information Density for Vision Transformers on Small Datasets
To boost the performance of vision Transformers on small datasets, this paper proposes to explicitly increase the input information density in the frequency domain.
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.
A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild
The fine-grained attention is achieved by utilizing the change in motion of the video frames (optical flow) in sequential context-based attention along with a Transformer encoder model.
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.
Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks
The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences.
