Adaptive Convolution for Object Detection

Abstract

It is quite challenging to detect objects, especially, small objects, in complex scenes. To solve this problem, we propose a novel module named as adaptive convolution block (ACB), which adaptively adjusts the parameters of convolutional filters according to the current feature maps, and then, filter these feature maps with the obtained adaptive convolutional filters to generate enhanced features. Due to such adaptive convolution, the enhanced features can pay more attention to the concerned objects, suppress the interference information caused by irrelevant surroundings, and efficiently improve the detection accuracy. The proposed ACB is light weight and fast. By directly embedding the ACB into the single shot detection framework, we construct a novel real-time adaptive convolutional detector (ACD). Experiments on PASCAL VOC and MS COCO benchmarks confirm that our ACD outperforms the existing state-of-the-art single-stage detection models, and achieves a better tradeoff between accuracy and speed.