Results 1 - 10 of 338

Rich feature hierarchies for accurate object detection and semantic segmentation

by Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
"... Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex en-semble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scala ..."
Abstract - Cited by 251 (23 self) - Add to MetaCart
and scalable detection algorithm that im-proves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012—achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural net-works (CNNs) to bottom-up region proposals

Weakly supervised object detection with posterior regularization

by Hakan Bilen , Marco Pedersoli , Tinne Tuytelaars - In British Machine Vision Conference , 2014
"... Abstract This paper focuses on the problem of object detection when the annotation at training time is restricted to presence or absence of object instances at image level. We present a method based on features extracted from a Convolutional Neural Network and latent SVM that can represent and expl ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
that the proposed method outperforms the state-of-the-art in weakly-supervised object detection and object classification on the Pascal VOC 2007 dataset.

Weakly Supervised Semantic Segmentation with Convolutional Networks

by Pedro O. Pinheiro, Ronan Collobert
"... We are interested in inferring object segmentation by leveraging only object class information, and by consider-ing only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly super-vised segmentation task, and naturally fits the Multiple In-stance Learning ..."
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We are interested in inferring object segmentation by leveraging only object class information, and by consider-ing only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly super-vised segmentation task, and naturally fits the Multiple In-stance Learning

Barrier coverage with wireless sensors

by Santosh Kumar, Ten H. Lai, Anish Arora - In ACM MobiCom , 2005
"... When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every ..."
Abstract - Cited by 137 (9 self) - Add to MetaCart
When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every

1Region-based Convolutional Networks for Accurate Object Detection and Segmentation

by Ross Girshick, Jeff Donahue, Student Member, Trevor Darrell, Jitendra Malik
"... Abstract—Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this pap ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R

Optimal linear cooperation for spectrum sensing in cognitive radio networks

by Zhi Quan, Shuguang Cui, Ali H. Sayed - IEEE J. SEL. TOPICS SIGNAL PROCESS , 2008
"... Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signal ..."
Abstract - Cited by 122 (8 self) - Add to MetaCart
Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect

Self-Taught Object Localization with Deep Networks

by Loris Bazzani, Alessandro Bergamo, Dragomir Anguelov, Lorenzo Torresani
"... The reliance on plentiful and detailed manual annota-tions for training is a critical limitation of the current state of the art in object localization and detection. This pa-per introduces self-taught object localization, a novel ap-proach that leverages deep convolutional networks trained for whol ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
than 22 % in both precision and re-call with respect to the state of the art (BING and Selective Search) for top-1 subwindow proposal. Our experiments on a challenging dataset of 200 classes indicate that our automatically-generated annotations are accurate enough to train object detectors in a weakly-supervised

Faster R-CNN: Towards real-time object detection with region proposal networks.

by Shaoqing Ren , Kaiming He , Ross Girshick , Jian Sun - In NIPS, , 2015
"... Abstract State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
Abstract State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet

Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video

by Yang Yang, Guang Shu, Mubarak Shah
"... We propose a novel approach to boost the performance of generic object detectors on videos by learning videospecific features using a deep neural network. The insight behind our proposed approach is that an object appearing in different frames of a video clip should share similar features, which can ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
We propose a novel approach to boost the performance of generic object detectors on videos by learning videospecific features using a deep neural network. The insight behind our proposed approach is that an object appearing in different frames of a video clip should share similar features, which

Recursive neural networks for object detection

by M. Bianchini, M. Maggini, L. Sarti, F. Scarselli - In Proceedings of the IEEE international joint conference on neural networks (IJCNN 2004 , 2004
"... Abstract — In this paper, a new recursive neural network model, able to process directed acyclic graphs with labeled edges, is introduced, in order to address the problem of object detection in images. In fact, the detection is a preliminary step in any object recognition system. The proposed method ..."
Abstract - Cited by 3 (1 self) - Add to MetaCart
Abstract — In this paper, a new recursive neural network model, able to process directed acyclic graphs with labeled edges, is introduced, in order to address the problem of object detection in images. In fact, the detection is a preliminary step in any object recognition system. The proposed
Results 1 - 10 of 338