Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for
object detection. This design enables the original ViT architecture to be fine-tuned for object …

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras
and LiDAR, have significant potential in precision agriculture, including object detection …

In this paper, we propose a novel query design for the transformer-based object detection. In
previous transformer-based detectors, the object queries are a set of learned embeddings …

Weeds are among the major threats to cotton production. Overreliance on herbicides for
weed control has accelerated the evolution of herbicide-resistance in weeds and caused …

This paper presents a novel parametric curve-based method for lane detection in RGB
images. Unlike state-of-the-art segmentation-based and point detection-based methods that …

Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …

In order to deal with the sparse and unstructured raw point clouds, most LiDAR based 3D
object detection research focuses on designing dedicated local point aggregators for fine …

In this paper, we propose a conceptually novel, efficient, and fully convolutional framework
for real-time instance segmentation. Previously, most instance segmentation methods …