Object Detection is the task of classification and localization of objects in an image or video.
It has gained prominence in recent years due to its widespread applications. This article …

Curriculum learning (CL) is a training strategy that trains a machine learning model from
easier data to harder data, which imitates the meaningful learning order in human curricula …

Recognition in low quality face datasets is challenging because facial attributes are
obscured and degraded. Advances in margin-based loss functions have resulted in …

Two-branch network architecture has shown its efficiency and effectiveness in real-time
semantic segmentation tasks. However, direct fusion of high-resolution details and low …

Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

In recent years, face detection algorithms based on deep learning have made great
progress. Nevertheless, the effective utilization of face detectors for small and occlusion …

Previous pseudo-label approaches for semi-supervised object detection typically follow a
multi-stage schema, with the first stage to train an initial detector on a few labeled data …

In object detection, bounding box regression (BBR) is a crucial step that determines the
object localization performance. However, we find that most previous loss functions for BBR …

Emerging interests have been brought to recognize previously unseen objects given very
few training examples, known as few-shot object detection (FSOD). Recent researches …