Models, code, and papers for "Jiangmiao Pang":

##### R$^2$-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images

Mar 30, 2019
Jiangmiao Pang, Cong Li, Jianping Shi, Zhihai Xu, Huajun Feng

Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. First, the extreme large input size makes the existing object detection solutions too slow for practical use. Second, the massive and complex backgrounds cause serious false alarms. Moreover, the ultratiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network ($\mathcal{R}^2$-CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector. Tiny-Net is a lightweight residual structure, which enables fast and powerful features extraction from inputs. Global attention block is built upon Tiny-Net to inhibit false positives. Classifier is then used to predict the existence of targets in each patch, and detector is followed to locate them accurately if available. The classifier and detector are mutually reinforced with end-to-end training, which further speed up the process and avoid false alarms. Effectiveness of $\mathcal{R}^2$-CNN is validated on hundreds of GF-1 images and GF-2 images that are 18 000 $\times$ 18 192 pixels, 2.0-m resolution, and 27 620 $\times$ 29 200 pixels, 0.8-m resolution, respectively. Specifically, we can process a GF-1 image in 29.4 s on Titian X just with single thread. According to our knowledge, no previous solution can detect the tiny object on such huge remote sensing images gracefully. We believe that it is a significant step toward practical real-time remote sensing systems.

* 13 pages. Accepted to IEEE Transactions on Geoscience and Remote Sensing
##### $\mathcal{R}^2$-CNN: Fast Tiny Object Detection in Large-scale Remote Sensing Images

Feb 16, 2019
Jiangmiao Pang, Cong Li, Jianping Shi, Zhihai Xu, Huajun Feng

Recently, convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. Firstly, the extreme large input size makes existing object detection solutions too slow for practical use. Secondly, the massive and complex backgrounds cause serious false alarms. Moreover, the ultra tiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called $\mathcal{R}^2$-CNN: Remote sensing Region-based Convolutional Neural Network, composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector. Tiny-Net is a lightweight residual structure which enables fast and powerful features extraction from inputs. Global attention block is built upon Tiny-Net to inhibit false positives. Classifier is then used to predict the existence of target in each patch, and detector is followed to locate them accurately if available. The classifier and detector are mutually reinforced with end-to-end training, which further speed-up the process and avoid false alarms. Effectiveness of $\mathcal{R}^2$-CNN is validated on hundreds of \emph{GF-1} images and \emph{GF-2} images, which are $18000 \times 18192$ pixels, 2.0m resolution, and $27620 \times 29200$ pixels, 0.8m resolution respectively. Specifically, we can process a GF-1 image in 29.4s on Titian X just with single thread. According to our knowledge, no previous solution can detect tiny object on such huge remote sensing images gracefully. We believe that it is a significant step towards practical real-time remote sensing systems.

* 13 pages. Accepted to IEEE Transactions on Geoscience and Remote Sensing
##### FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction

Jan 11, 2019
Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang

The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e.g., image-level, region-level, and pixel-level are diverging. Generally, network structures designed specifically for image classification are directly used as default backbone structure for other tasks including detection and segmentation, but there is seldom backbone structure designed under the consideration of unifying the advantages of networks designed for pixel-level or region-level predicting tasks, which may require very deep features with high resolution. Towards this goal, we design a fish-like network, called FishNet. In FishNet, the information of all resolutions is preserved and refined for the final task. Besides, we observe that existing works still cannot \emph{directly} propagate the gradient information from deep layers to shallow layers. Our design can better handle this problem. Extensive experiments have been conducted to demonstrate the remarkable performance of the FishNet. In particular, on ImageNet-1k, the accuracy of FishNet is able to surpass the performance of DenseNet and ResNet with fewer parameters. FishNet was applied as one of the modules in the winning entry of the COCO Detection 2018 challenge. The code is available at https://github.com/kevin-ssy/FishNet.

* NeurIPS 2018. Code available at https://github.com/kevin-ssy/FishNet
##### Libra R-CNN: Towards Balanced Learning for Object Detection

Apr 04, 2019
Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, Dahua Lin

Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple but effective framework towards balanced learning for object detection. It integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level. Benefitted from the overall balanced design, Libra R-CNN significantly improves the detection performance. Without bells and whistles, it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN Faster R-CNN and RetinaNet respectively on MSCOCO.

* To appear at CVPR 2019
##### Side-Aware Boundary Localization for More Precise Object Detection

Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regression remains unsatisfactory, hence limiting performance in object detection. We observe that precise localization requires careful placement of each side of the bounding box. However, the mainstream approach, which focuses on predicting centers and sizes, is not the most effective way to accomplish this task, especially when there exists displacements with large variance between the anchors and the targets.In this paper, we propose 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. Moreover, to tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket. We test the proposed method on both two-stage and single-stage detection frameworks. Replacing the standard bounding box regression branch with the proposed design leads to significant improvements on Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.6%, and 0.9%, respectively. Code and models will be available at https://github.com/open-mmlab/mmdetection.

* Technical Report