Object detection in remotely sensed satellite pictures is fundamental in many fields such as
biophysical and environmental monitoring. While deep learning algorithms are constantly …

Quality assessment in industrial applications is often carried out through visual inspection,
usually performed or supported by human domain experts. However, the manual visual …

Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal
component within the intelligent development path of agricultural information technology …

We present, a versatile multi-modal large language model (MLLM) with a joint mixing of
model weights, visual embeddings and image scales. First, for stronger vision-language …

Due to the global population increase and the recovery of agricultural demand after the
COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural …

Deep learning has become the preferred method for automated object detection, but the
accurate detection of small objects remains a challenge due to the lack of distinctive …

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal
yield prediction and resource allocation. Traditional crop growth monitoring methods are …

This article introduces Lester, a novel method to automatically synthesize retro-style 2D
animations from videos. The method approaches the challenge mainly as an object …

This study investigates the efficacy of an open vocabulary, multi-modal, foundation model for
the semantic segmentation of images from complex urban street scenes. Unlike traditional …

The recent surge in diverse 3D datasets spanning various scales and applications marks a
significant advancement in the field. However, the comprehensive process of data …