Jingkai Zhou

TL;DR: A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.

Abstract: Convolution is one of the basic building blocks of CNN architectures. Despite its common use, standard convolution has two main shortcomings: Content-agnostic and Computation-heavy. Dynamic filters are content-adaptive, while further increasing the computational overhead. Depth-wise convolution is a lightweight variant, but it usually leads to a drop in CNN performance or requires a larger number of channels. In this work, we propose the Decoupled Dynamic Filter (DDF) that can simultaneously tackle both of these shortcomings. Inspired by recent advances in attention, DDF decouples a depth-wise dynamic filter into spatial and channel dynamic filters. This decomposition considerably reduces the number of parameters and limits computational costs to the same level as depth-wise convolution. Meanwhile, we observe a significant boost in performance when replacing standard convolution with DDF in classification networks. ResNet50 / 101 get improved by 1.9% and 1.3% on the top-1 accuracy, while their computational costs are reduced by nearly half. Experiments on the detection and joint upsampling networks also demonstrate the superior performance of the DDF upsampling variant (DDF-Up) in comparison with standard convolution and specialized content-adaptive layers. The project page with code is available 1.

TL;DR: The Vision Meets Drone Object Detection in Image Challenge (VME-DET 2019) as discussed by the authors, held in conjunction with the 17th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones.

TL;DR: The goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them and demonstrate that there still remains a large room for improvement for object detection algorithms on drones.

TL;DR: A nighttime FIR pedestrian dataset with the largest scale at present is introduced in this paper, which is called SCUT (South China University of Technology) dataset and shows that convolutional neural networks (CNN) based detectors obtained good performance on FIR image.