segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random Field, in which each detection hypothesis scores object appearance as well as contextual information using Convolutional Neural Networks, and allows the hypothesis to choose and score a segment out of a large pool of accurate object segmentation proposals. This enables the detector to incorporate additional evidence when it is available and thus results in more accurate detections. Our experiments show an improvement of 4.1% in mAP over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current state-of-the-art, demonstrating the power of our approach

Improving region based CNN object detector using bayesian optimization

Using Deep Neural Networks for object detection tasks has had groundbreaking results on several object detection benchmarks. Although the trained models have high capacity and strong discrimination power, yet inaccurate localization is a major source of error for those detection systems. In my work, I\u27m developing a sequential searching algorithm based on Bayesian Optimization to propose better candidate bounding boxes for the objects of interest. The work is focusing on formulating effective region proposal as an optimization problem and using Bayesian Optimization algorithm as a black-box optimizer to sequentially solve this problem. The proposed algorithm demonstrated the state-of-the-art performance on PASCAL VOC 2007 benchmark under the standard localization requirements

Scalable Content-Based Analysis of Images in Web Archives with TensorFlow and the Archives Unleashed Toolkit

We demonstrate the integration of the Archives Unleashed Toolkit, a scalable platform for exploring web archives, with Google's TensorFlow deep learning toolkit to provide scholars with content-based image analysis capabilities. By applying pretrained deep neural networks for object detection, we are able to extract images of common objects from a 4TB web archive of GeoCities, which we then compile into browsable collages. This case study illustrates the types of interesting analyses enabled by combining big data and deep learning capabilities.This work was primarily supported by the Natural Sciences and Engineering Research Council of Canada. Additional funding for this project has come from the Andrew W. Mellon Foundation. Our sincerest thanks to the Internet Archive for providing us with the GeoCities web archive

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure