Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

The large pre-trained vision transformers (ViTs) have demonstrated remarkable
performance on various visual tasks, but suffer from expensive computational and memory …

The recent detection transformer (DETR) has advanced object detection, but its application
on resource-constrained devices requires massive computation and memory resources …

Binary neural networks (BNNs) have received ever-increasing popularity for their great
capability of reducing storage burden as well as quickening inference time. However, there …

Neural architecture search (NAS) proves to be among the effective approaches for many
tasks by generating an application-adaptive neural architecture, which is still challenged by …

Recently, the Bird's-Eye-View (BEV) representation has gained increasing attention in multi-
view 3D object detection, demonstrating promising applications in autonomous driving …

Collecting diverse sets of training images for RGB-D semantic image segmentation is not
always possible. In particular, when robots need to operate in privacy-sensitive areas like …

Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying
large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) that …

Vision Transformers (ViTs) have emerged as the new fundamental architecture for most
computer vision fields. However, the considerable memory and computation costs also …

Recent research in remote-sensing object detection (RSOD) has significantly advanced the
development of vision foundation models. However, deploying these models on resource …