Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing
methods track by associating detections through motion-based and appearance-based …

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about
track initialization, identity, and spatio-temporal trajectories. We formulate this task as a …

Tracking has traditionally been the art of following interest points through space and time.
This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Sentence embeddings are an important component of many natural language processing
(NLP) systems. Like word embeddings, sentence embeddings are typically learned on large …

Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …

Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …

As an important area in computer vision, object tracking has formed two separate
communities that respectively study Single Object Tracking (SOT) and Multiple Object …