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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TLDR
Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.Abstract:
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.read more
Citations
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Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training.
TL;DR: After pretraining on large-scale image-caption pairs, Unicoder-VL is transferred to caption-based image-text retrieval and visual commonsense reasoning, with just one additional output layer, and shows the powerful ability of the cross-modal pre-training.
Journal ArticleDOI
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
Dushyant Mehta,Srinath Sridhar,Oleksandr Sotnychenko,Helge Rhodin,Mohammad Shafiei,Hans-Peter Seidel,Weipeng Xu,Dan Casas,Christian Theobalt +8 more
TL;DR: This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.
Proceedings ArticleDOI
Real-Time Seamless Single Shot 6D Object Pose Prediction
TL;DR: A single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses is proposed, which substantially outperforms other recent CNN-based approaches when they are all used without postprocessing.
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Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
TL;DR: In this paper, two new strategies to detect objects accurately and efficiently using deep convolutional neural network are investigated: scale-dependent pooling and layerwise cascaded rejection classifiers.
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Character Region Awareness for Text Detection
TL;DR: Zhang et al. as mentioned in this paper proposed a new scene text detection method to effectively detect text area by exploring each character and affinity between characters, which significantly outperforms the state-of-the-art detectors.
References
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