With the rapid development of deep learning, many deep learning-based approaches have
made great achievements in object detection tasks. It is generally known that deep learning …

This work investigates a simple yet powerful adapter for Vision Transformer (ViT). Unlike
recent visual transformers that introduce vision-specific inductive biases into their …

This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out
that the success of FPN is due to its divide-and-conquer solution to the optimization problem …

Abstract We present Boundary IoU (Intersection-over-Union), a new segmentation
evaluation measure focused on boundary quality. We perform an extensive analysis across …

This paper revisits the standard pretrain-then-finetune paradigm used in computer vision for
visual recognition tasks. Typically, state-of-the-art foundation models are pretrained using …

Estimating 3D bounding boxes from monocular images is an essential component in
autonomous driving, while accurate 3D object detection from this kind of data is very …

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

Object detection is a central downstream task used to test if pre-trained network parameters
confer benefits, such as improved accuracy or training speed. The complexity of object …

Recent advances in detecting arbitrary objects in the real world are trained and evaluated
on object detection datasets with a relatively restricted vocabulary. To facilitate the …

Can our video understanding systems perceive objects when a heavy occlusion exists in a
scene? To answer this question, we collect a large-scale dataset called OVIS for occluded …