In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

As a common image editing operation, image composition aims to combine the foreground
from one image and another background image, resulting in a composite image. However …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

Building instance segmentation models that are data-efficient and can handle rare object
categories is an important challenge in computer vision. Leveraging data augmentations is a …

3D semantic scene labeling is fundamental to agents operating in the real world. In
particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent …

A major impediment in rapidly deploying object detection models for instance detection is
the lack of large annotated datasets. For example, finding a large labeled dataset containing …

What constitutes an object? This has been a long-standing question in computer vision.
Towards this goal, numerous learning-free and learning-based approaches have been …

Despite increasing efforts on universal representations for visual recognition, few have
addressed object detection. In this paper, we develop an effective and efficient universal …

We introduce T-LESS, a new public dataset for estimating the 6D pose, ie translation and
rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with …

Detection of objects in cluttered indoor environments is one of the key enabling
functionalities for service robots. The best performing object detection approaches in …