An open set model for pest identification.

Agricultural pest identification is a prerequisite for increasing crop production and meeting global food demands. Generally, numerous phenotypic and genotypic features are widely utilized for species-level pest identification. However, the approaches are time-consuming and require expert knowledge in relevant fields.

Mitigating carbon footprint for knowledge distillation based deep learning model compression.

Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step.

Polarity Loss: Improving Visual-Semantic Alignment for Zero-Shot Detection.

Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behavior, zero-shot object detection (ZSD) aims to recognize and localize "unseen" object instances by using only their semantic information.

Ocular Blood Volume Index Based on Scattering Properties of Retinal Vessels Using Spectral Domain Optical Coherence Tomography.

The evaluation of retinal vessels and the retinal blood flow is important for ocular diseases. We introduce a spectral domain optical coherence tomography (SD-OCT) based method for facilitating a retinal blood vessel analysis using the scattering properties of retinal vessels. The intensity of the distal shadow of vessels caused by the scattered signal is measured, correlated with the pulsatile ocular blood flow (POBF), and its repeatability is analyzed.

A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning.

Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot and few-shot learning problems. Our approach is based on a novel Class Adapting Principal Directions (CAPD) concept that allows multiple embeddings of image features into a semantic space.

Optical Coherence Tomography-Based Scattering Properties of Retinal Vessels in Glaucoma Patients.

Purpose: Clinical trials have demonstrated that retinal blood flow deficiencies are present in patients with open-angle glaucoma (OAG). We introduce a method for facilitating retinal vessel analysis: The intensity of the distal shadow of vessels in optical coherence tomography (OCT) caused by the scattered signal is analyzed, compared between healthy subjects and OAG patients and correlated with OCT angiography (OCT-A) flow density.

Patients And Methods: We recruited 80 patients with diagnosed OAG (mean age 63.

An Automated Approach for Localizing Retinal Blood Vessels in Confocal Scanning Laser Ophthalmoscopy Fundus Images.

In this work, we present a rules-based method for localizing retinal blood vessels in confocal scanning laser ophthalmoscopy (cSLO) images and evaluate its feasibility. A total of 31 healthy participants (17 female; mean age: 64.0 ± 8.

Visual Saliency Prediction and Evaluation across Different Perceptual Tasks.

Saliency maps produced by different algorithms are often evaluated by comparing output to fixated image locations appearing in human eye tracking data. There are challenges in evaluation based on fixation data due to bias in the data. Properties of eye movement patterns that are independent of image content may limit the validity of evaluation results, including spatial bias in fixation data.

On computational modeling of visual saliency: Examining what's right, and what's left.

In the past decade, a large number of computational models of visual saliency have been proposed. Recently a number of comprehensive benchmark studies have been presented, with the goal of assessing the performance landscape of saliency models under varying conditions. This has been accomplished by considering fixation data, annotated image regions, and stimulus patterns inspired by psychophysics.