Common object counting in a natural scene is a challenging problem in computer vision with numerous real-world applications. Existing image-level supervised common object counting approaches only predict the global object count and rely on additional instance-level supervision to also determine object locations. We propose an image-level supervised approach that provides both the global object count and the spatial distribution of object instances by constructing an object category density map. Motivated by psychological studies, we further reduce image-level supervision using a limited object count information (up to four). To the best of our knowledge, we are the first to propose image-level supervised density map estimation for common object counting and demonstrate its effectiveness in image-level supervised instance segmentation. Comprehensive experiments are performed on the PASCAL VOC and COCO datasets. Our approach outperforms existing methods, including those using instance-level supervision, on both datasets for common object counting. Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17.8 in terms of average best overlap, on the PASCAL VOC 2012 dataset. Code link: this https URL
Chattopadhyay al @cite_18 investigated regression-based common object counting, using image-level (per-category count) and instance-level (bounding box) supervisions. The image-level supervised strategy, denoted as glancing, used count annotations from both within and beyond the subitizing range to predict the global count of objects, without providing information about their location. The instance-level (bounding box) supervised strategy, denoted as subitizing, estimated a large number of objects by dividing an image into non-overlapping regions, assuming the object count in each region falls within the subitizing range. Instead, our ILC supervised approach requires neither bounding box annotation nor beyond subitizing range count information during training. It then predicts the global object count, even beyond the subitizing range, together with the spatial distribution of object instances.
@cite_18
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We are interested in counting the number of instances of object classes in natural, everyday images. Previous counting approaches tackle the problem in restricted domains such as counting pedestrians in surveillance videos. Counts can also be estimated from outputs of other vision tasks like object detection. In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes. Our approach is inspired by the phenomenon of subitizing &#x2013; the ability of humans to make quick assessments of counts given a perceptual signal, for small count values. Given a natural scene, we employ a divide and conquer strategy while incorporating context across the scene to adapt the subitizing idea to counting. Our approach offers consistent improvements over numerous baseline approaches for counting on the PASCAL VOC 2007 and COCO datasets. Subsequently, we study how counting can be used to improve object detection. We then show a proof of concept application of our counting methods to the task of Visual Question Answering, by studying the how many? questions in the VQA and COCO-QA datasets.