From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

TL;DR

This paper introduces S-DCNet, a novel spatial divide-and-conquer approach for counting objects in images that generalizes from limited closed-set training data to open-set scenarios, achieving state-of-the-art results.

Contribution

The paper proposes S-DCNet, a simple and efficient method that decomposes counting tasks into sub-regions, enabling generalization from closed-set training to open-set counting problems.

Findings

Achieves state-of-the-art performance on multiple counting datasets.

Provides a 20-22% relative improvement over previous methods.

Efficiently generalizes to unseen count ranges through spatial divide-and-conquer.

Abstract

Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in $[0,+\infty)$ in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and- Conquer Network (S-DCNet). S-DCNet only learns from a closed set but can generalize well to open-set scenarios via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github. com/xhp-hust-2018-2011/S-DCNet.