Object detection using deep learning has attracted considerable interest from researchers
because of its competency in performing state-of-the-art tasks, including detection …

Localizing individuals in crowds is more in accordance with the practical demands of
subsequent high-level crowd analysis tasks than simply counting. However, existing …

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 …

Class-agnostic counting (CAC) aims to count all instances in a query image given few
exemplars. A standard pipeline is to extract visual features from exemplars and match them …

Rice plant counting is crucial for many applications in rice production, such as yield
estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still …

In computer vision, it is often observed that formulating regression problems as a
classification task often yields better performance. We investigate this curious phenomenon …

We formulate counting as a sequential decision problem and present a novel crowd
counting model solvable by deep reinforcement learning. In contrast to existing counting …

One of appealing approaches to counting dense objects, such as crowd, is density map
estimation. Density maps, however, present ambiguous appearance cues in congested …

Fast and accurate plant counting tools affect revolution in modern agriculture. Agricultural
practitioners, however, expect the output of the tools to be not only accurate but also …

Plant counting runs through almost every stage of agricultural production from seed
breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting …