Yuhang Cao

TL;DR: This paper presents MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules, and conducts a benchmarking study on different methods, components, and their hyper-parameters.

TL;DR: The notion of Prime Samples, those that play a key role in driving the detection performance are proposed, and a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) is developed that directs the focus of the training process towards such samples.

TL;DR: This paper proposes an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch, to tackle the difficulty of precise localization in the presence of displacements with large variance.

TL;DR: This work proposes Seesaw Loss to dynamically re-balance gradients of positive and negative samples for each category, with two complementary factors, i.e., mitigation factor and compensation factor, which obtains significant gains over Cross-Entropy Loss and achieves state-of-the-art performance on LVIS dataset without bells and whistles.

TL;DR: Seesaw Loss as discussed by the authors dynamically re-balance gradients of positive and negative samples for each category, with two complementary factors, i.e., mitigation factor and compensation factor.