Open AccessProceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- Vol. 25, pp 1097-1105
TLDR
The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.Abstract:
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.read more
Citations
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Proceedings ArticleDOI
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Leonid Pishchulin,Eldar Insafutdinov,Siyu Tang,Bjoern Andres,Mykhaylo Andriluka,Peter V. Gehler,Bernt Schiele +6 more
TL;DR: An approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other is proposed.
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DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
Eldar Insafutdinov,Leonid Pishchulin,Bjoern Andres,Mykhaylo Andriluka,Mykhaylo Andriluka,Bernt Schiele +5 more
TL;DR: In this article, the authors proposed an improved body part detector that generates effective bottom-up proposals for body parts, image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations, and an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speedup factors.
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Learning to Detect Salient Objects with Image-Level Supervision
TL;DR: This paper develops a weakly supervised learning method for saliency detection using image-level tags only, which outperforms unsupervised ones with a large margin, and achieves comparable or even superior performance than fully supervised counterparts.
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Future Frame Prediction for Anomaly Detection - A New Baseline
TL;DR: In this article, Liu et al. propose to detect abnormal events by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task.
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BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Jifeng Dai,Kaiming He,Jian Sun +2 more
TL;DR: This paper proposes a method that achieves competitive accuracy but only requires easily obtained bounding box annotations, and yields state-of-the-art results on PASCAL VOC 2012 and PASCal-CONTEXT.
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