We propose a dense object detector with an instance-wise sampling strategy, named IQDet.
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.
Ranked #38 on Object Detection on COCO test-dev
The teacher's weight is a momentum update of the student, and the teacher's BN statistics is a momentum update of those in history.
A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.
Our Faster R-CNN (ResNet50-FPN) baseline achieves 39. 8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet.
In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Ranked #6 on Few-Shot Object Detection on MS-COCO (30-shot)
During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.
Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.
Ranked #100 on Object Detection on COCO test-dev
Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.
Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.