In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

In object detection, an intersection over union (IoU) threshold is required to define positives
and negatives. An object detector, trained with low IoU threshold, eg 0.5, usually produces …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

Feature pyramids are a basic component in recognition systems for detecting objects at
different scales. But pyramid representations have been avoided in recent object detectors …

Cascade is a classic yet powerful architecture that has boosted performance on various
tasks. However, how to introduce cascade to instance segmentation remains an open …

Scale variation is one of the key challenges in object detection. In this work, we first present
a controlled experiment to investigate the effect of receptive fields for scale variation in …

Modern CNN-based object detectors rely on bounding box regression and non-maximum
suppression to localize objects. While the probabilities for class labels naturally reflect …

Region anchors are the cornerstone of modern object detection techniques. State-of-the-art
detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly …

In this work we train in an end-to-end manner a convolutional neural network (CNN) that
jointly handles low-, mid-, and high-level vision tasks in a unified architecture. Such a …

The past few years have witnessed the immense success of object detection, while current
excellent detectors struggle on tackling size-limited instances. Concretely, the well-known …