We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

Detecting objects and estimating their viewpoints in images are key tasks of 3D scene
understanding. Recent approaches have achieved excellent results on very large …

With the advent of deep learning, object detection drifted from a bottom-up to a top-down
recognition problem. State of the art algorithms enumerate a near-exhaustive list of object …

Detection identifies objects as axis-aligned boxes in an image. Most successful object
detectors enumerate a nearly exhaustive list of potential object locations and classify each …

DoF object pose estimation from a single RGB image is a fundamental and long-standing
problem in computer vision. Current leading approaches solve it by training deep networks …

We introduce HybridPose, a novel 6D object pose estimation approach. HybridPose utilizes
a hybrid intermediate representation to express different geometric information in the input …

We present a method that can recognize new objects and estimate their 3D pose in RGB
images even under partial occlusions. Our method requires neither a training phase on …

Existing works on 2D pose estimation mainly focus on a certain category, eg human, animal,
and vehicle. However, there are lots of application scenarios that require detecting the …

Face detection and alignment in unconstrained environment is always deployed on edge
devices which have limited memory storage and low computing power. This paper proposes …

Despite rapid progress in Visual question answering (\textit {VQA}), existing datasets and
models mainly focus on testing reasoning in 2D. However, it is important that VQA models …