Object Detection Methods using Deep Learning

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The document discusses object detection techniques including R-CNN, SPPnet, Fast R-CNN, and Faster R-CNN. R-CNN uses region proposals and CNN features to classify each region. SPPnet improves efficiency by computing CNN features once for the whole image. Fast R-CNN further improves efficiency by sharing computation and using a RoI pooling layer. Faster R-CNN introduces a region proposal network to generate proposals, achieving end-to-end training. The techniques showed improved accuracy and processing speed over prior methods.

Object detection
Sungjoon Choi
(sungjoon.choi@cpslab.snu.ac.kr)

R-CNN
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Image Regions Resize Convolution
Features
Classify

SPP net
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Image Convolution Features SPPRegions Classify

Fast R-CNN
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Image
Convolution Features
Regions
RoI Pooling
Layer
Class Label
Confidence
RoI Pooling
Layer
Class Label
Confidence

R-CNN vs. SPP net vs. Fast R-CNN
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R-CNN SPP net
Fast R-CNN

Faster R-CNN
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Image Fully Convolutional
Features
Bounding Box
Regression
BB Classification
FastR-CNN

R-CNN vs. SPP net vs. Fast R-CNN
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Object Detection Methods using Deep Learning

Object detection is an important computer vision technique with applications in several domains such as autonomous driving, personal and industrial robotics. The below slides cover the history of object detection from before deep learning until recent research. The slides aim to cover the history and future directions of object detection, as well as some guidelines for how to choose which type of object detector to use for your own project.

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Slides by Amaia Salvador at the UPC Computer Vision Reading Group. Source document on GDocs with clickable links: https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing Based on the original work: Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.

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RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...

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ongomchris

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Teleport Manpower Consultant

Immunizing Image Classifiers Against Localized Adversary Attacks

gerogepatton

This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks (CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations. When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10 and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing accuracy improvements over previous techniques. The results indicate that the combination of the volumetric input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating adversary training.

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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)

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