  Despite advancements in generic object detection, there remains a performance
gap in detecting small objects compared to normal-scale objects. We for the
first time observe that existing bounding box regression methods tend to
produce distorted gradients for small objects and result in less accurate
localization. To address this issue, we present a novel Confidence-driven
Bounding Box Localization (C-BBL) method to rectify the gradients. C-BBL
quantizes continuous labels into grids and formulates two-hot ground truth
labels. In prediction, the bounding box head generates a confidence
distribution over the grids. Unlike the bounding box regression paradigms in
conventional detectors, we introduce a classification-based localization
objective through cross entropy between ground truth and predicted confidence
distribution, generating confidence-driven gradients. Additionally, C-BBL
describes a uncertainty loss based on distribution entropy in labels and
predictions to further reduce the uncertainty in small object localization. The
method is evaluated on multiple detectors using three object detection
benchmarks and consistently improves baseline detectors, achieving
state-of-the-art performance. We also demonstrate the generalizability of C-BBL
to different label systems and effectiveness for high resolution detection,
which validates its prospect as a general solution.

Computer Vision and Pattern Recognition