Scale variation is a deep-rooted problem in object counting, which has not been effectively
addressed by existing scale-aware algorithms. An important factor is that they typically …

Counting high‐density objects quickly and accurately is a popular area of research. Crowd
counting has significant social and economic value and is a major focus in artificial …

We show that crowd counting can be viewed as a decomposable point querying process.
This formulation enables arbitrary points as input and jointly reasons whether the points are …

Crowd counting is a fundamental problem in crowd analysis which is typically accomplished
by estimating a crowd density map and summing over the density values. However this …

Recently, indiscernible scene understanding has attracted a lot of attention in the vision
community. We further advance the frontier of this field by systematically studying a new …

This paper tackles the problem of object counting in images. Existing approaches rely on
extensive training data with point annotations for each object, making data collection labor …

The current 3D human pose estimators face challenges in adapting to new datasets due to
the scarcity of 2D-3D pose pairs in target domain training sets. We present the Multi …

In real-world crowd counting applications, the crowd densities in an image vary greatly.
When facing density variation, humans tend to locate and count the targets in low-density …

Crowd counting and localization have become increasingly important in computer vision
due to their wide-ranging applications. While point-based strategies have been widely used …

Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a
template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it …