Pedestrian detection is an important branch of computer vision, and has important
applications in the fields of autonomous driving, artificial intelligence and video surveillance …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

This paper presents a Simple and effective unsupervised adaptation method for Robust
Object Detection (SimROD). To overcome the challenging issues of domain shift and …

Precision crop safety relies on automated systems for detecting and classifying plants. This
work proposes the detection and classification of nine species of plants of the PlantVillage …

Recent studies have recently exploited knowledge distillation (KD) technique to address
time-consuming annotation task in semantic segmentation, through which one teacher …

This paper proposes a new method for addressing the problem of unsupervised domain
adaptation for robust object detection. To this end, we propose an energy-based curriculum …

Extremely variant image pairs include distorted, deteriorated, and corrupted scenes that
have experienced severe geometric, photometric, or non-geometric-non-photometric …

Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without
access to source data after deployment. Existing approaches typically rely on self-training …

The detection of traffic signs in clean and noise-free images has been investigated by
numerous researchers; however, very few of these works have focused on noisy …

The electroencephalogram (EEG) is a tool utilized to capture the intricate electrical dynamics
within the brain, offering invaluable insights into neural activity. This method is pivotal in …