[PDF][PDF] YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems
This is a comprehensive review of the YOLO series of systems. Different from previous
literature surveys, this review article reexamines the characteristics of the YOLO series from …
literature surveys, this review article reexamines the characteristics of the YOLO series from …
Foundation models in robotics: Applications, challenges, and the future
We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …
learning models in robotics are trained on small datasets tailored for specific tasks, which …
Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively
Abstract The CLIP and Segment Anything Model (SAM) are remarkable vision foundation
models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is …
models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is …
Cnos: A strong baseline for cad-based novel object segmentation
We propose a simple yet powerful method to segment novel objects in RGB images from
their CAD models. Leveraging recent foundation models, Segment Anything and DINOv2 …
their CAD models. Leveraging recent foundation models, Segment Anything and DINOv2 …
IRSAM: Advancing segment anything model for infrared small target detection
Abstract The recent Segment Anything Model (SAM) is a significant advancement in natural
image segmentation, exhibiting potent zero-shot performance suitable for various …
image segmentation, exhibiting potent zero-shot performance suitable for various …
Open-vocabulary semantic segmentation with decoupled one-pass network
Recently, the open-vocabulary semantic segmentation problem has attracted increasing
attention and the best performing methods are based on two-stream networks: one stream …
attention and the best performing methods are based on two-stream networks: one stream …
Advances and challenges in deep learning-based change detection for remote sensing images: A review through various learning paradigms
Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting
changes in the Earth's surface, finding wide applications in urban planning, disaster …
changes in the Earth's surface, finding wide applications in urban planning, disaster …
Sam-assisted remote sensing imagery semantic segmentation with object and boundary constraints
Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise
information for diverse downstream applications. Recent development of the segment …
information for diverse downstream applications. Recent development of the segment …
Adapting segment anything model for change detection in VHR remote sensing images
Vision foundation models (VFMs), such as the segment anything model (SAM), allow zero-
shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety …
shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety …
Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models
As autonomous driving technology matures, end-to-end methodologies have emerged as a
leading strategy, promising seamless integration from perception to control via deep …
leading strategy, promising seamless integration from perception to control via deep …