Pedestrian detection is an important branch of computer vision, and has important
applications in the fields of autonomous driving, artificial intelligence and video surveillance …

In this paper, we propose a new query-based detection framework for crowd detection.
Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be …

Pedestrian detection is an important but challenging problem in computer vision, especially
in human-centric tasks. Over the past decade, significant improvement has been witnessed …

Object detection serves as one of most fundamental computer vision tasks. Existing works
on object detection heavily rely on dense object candidates, such as anchor boxes pre …

Object detection has recently achieved a breakthrough for removing the last one non-
differentiable component in the pipeline, Non-Maximum Suppression (NMS), and building …

Object detection is an important task that finds its application in a wide range of scenarios.
Generally, it requires extensive labels for training, which is quite time-consuming, especially …

Fire is a significant security threat that can lead to casualties, property damage, and
environmental damage. Despite the availability of object-detection algorithms, challenges …

Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead
to a lower recall rate and a higher threshold introduces more false positives. This problem is …

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-
occluding human instances, which brings ambiguity and optimization difficulties to the …

The detection performance in crowd scenes is limited by recalling hard objects (eg,
occluded objects). It requires that this kind of objects can be successfully detected and …