Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations
In crowd counting datasets, the location labels are costly, yet, they are not taken into the evaluation metrics. Besides, existing multi-task approaches employ high-level tasks to improve counting accuracy. This research tendency increases the demand for more annotations. In this paper, we propose a weakly-supervised counting network, which directly regresses the crowd numbers without the location supervision. Moreover, we train the network to count by exploiting the relationship among the images. We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers. The sorting network drives the shared backbone CNN model to obtain density-sensitive ability explicitly. Therefore, the proposed method improves the counting accuracy by utilizing the information hidden in crowd numbers, rather than learning from extra labels, such as locations and perspectives.We evaluate our proposed method on three crowd counting datasets, and the performance of our method plays favorably against the fully supervised state-of-the-art approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
Crowd CountingSimilar Papers 제목 키워드 기반
TransCrowd: weakly-supervised crowd counting with transformers
The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and lab…
Crowd CountingWSCF-MVCC: Weakly-supervised Calibration-free Multi-view Crowd Counting
Multi-view crowd counting can effectively mitigate occlusion issues that commonly arise in single-image crowd counting. Existing deep-learning multi-view crowd counting methods project different camera view images onto a…
Crowd CountingTowards Using Count-level Weak Supervision for Crowd Counting
Most existing crowd counting methods require object location-level annotation, i.e., placing a dot at the center of an object. While being simpler than the bounding-box or pixel-level annotation, obtaining this annotatio…
Crowd CountingJoint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting
Currently, for crowd counting, the fully supervised methods via density map estimation are the mainstream research directions. However, such methods need location-level annotation of persons in an image, which is time-co…
Crowd CountingWeakly-supervised LearningReinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting
Fully-supervised crowd counting is a laborious task due to the large amounts of annotations. Few works focus on weekly-supervised crowd counting, where only the global crowd numbers are available for training. The main c…
Crowd Counting