Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
Reads0
Chats0
TLDR
In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.Abstract:
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.read more
Citations
More filters
Journal ArticleDOI
A systematic study of the class imbalance problem in convolutional neural networks
TL;DR: The effect of class imbalance on classification performance is detrimental; the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; and thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.
Proceedings ArticleDOI
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
TL;DR: The authors balance the VQA dataset by collecting complementary images such that every question in the balanced dataset is associated with not just a single image, but rather a pair of similar images that result in two different answers to the same question.
Proceedings ArticleDOI
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Sixiao Zheng,Jiachen Lu,Hengshuang Zhao,Xiatian Zhu,Zekun Luo,Yabiao Wang,Yanwei Fu,Jianfeng Feng,Tao Xiang,Philip H. S. Torr,Li Zhang +10 more
TL;DR: Zhang et al. as discussed by the authors proposed a pure transformer to encode an image as a sequence of patches, which can be combined with a simple decoder to provide a powerful segmentation model.
Proceedings ArticleDOI
Training Region-Based Object Detectors with Online Hard Example Mining
TL;DR: In this article, the authors proposed an online hard example mining (OHEM) algorithm for training region-based ConvNet detectors and achieved state-of-the-art results.
Journal ArticleDOI
Random Erasing Data Augmentation
TL;DR: Random Erasing as mentioned in this paper randomly selects a rectangle region in an image and erases its pixels with random values, which reduces the risk of overfitting and makes the model robust to occlusion.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: 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.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).