Fetching the paper…
Reading the bibliography…
In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features.
Standardization of progressive matrices, 1938
Raven, J.C.: · 1941
Earlier work this paper cites.
A real-time algorithm for signal analysis with the help of the wavelet transform
Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: · 1990
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
LeCun, Y., Bengio, Y., et al.: · 1995
Earlier work this paper cites.
A computational analysis of the apprehension of spatial relations
Logan, G.D., Sadler, D.D.: · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Y.: · 1998
Earlier work this paper cites.
The discovery of structural form
Kemp, C., Tenenbaum, J.B.: · 2008
Earlier work this paper cites.
Neurocognitive development of relational reasoning
Crone, E.A., Wendelken, C., Van Leijenhorst, L., Honomichl, R.D., Christoff, K., Bunge, S.A.: · 2009
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., Lee, H.: · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
Beyond spatial pyramids: Receptive field learning for pooled image features
Jia, Y., Huang, C., Darrell, T.: · 2012
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Better computer go player with neural network and long-term prediction
Tian, Y., Zhu, Y.: · 2015
Cited alongside, same era.
Proposal-free network for instance-level object segmentation
Liang, X., Wei, Y., Shen, X., Yang, J., Lin, L., Yan, S.: · 2015
Cited alongside, same era.
Word sense disambiguation using a bidirectional lstm
Kågebäck, M., Salomonsson, H.: · 2016
Later among the works it cites.
Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: · 2017
Closest in time.
Discovering objects and their relations from entangled scene representations
Raposo, D., Santoro, A., Barrett, D., Pascanu, R., Lillicrap, T., Battaglia, P.: · 2017
Closest in time.
A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D.G., Malinowski, M., Pascanu, R., Battaglia, P., Lillicrap, T.: · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yu, F., Koltun, V.: · 2015
Cited alongside, same era.
Training very deep networks
Srivastava, R.K., Greff, K., Schmidhuber, J.: · 2015
Cited alongside, same era.
Understanding the effective receptive field in deep convolutional neural networks
Luo, W., Li, Y., Urtasun, R., Zemel, R.: · 2016
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C.L., Girshick, R.: · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., Sun, J.: · 2016
Cited alongside, same era.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Hu, J., Shen, L., Sun, G.: · 2017
Closest in time.
Active convolution: Learning the shape of convolution for image classification
Jeon, Y., Kim, J.: · 2017
Closest in time.
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: · 2017
Closest in time.
Pytorch implementation of deformable convolution
OUYANG, W.: · 2017
Closest in time.
Analysis and optimization of convolutional neural network architectures
Thoma, M.: · 2017
Closest in time.
Inferring and executing programs for visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Hoffman, J., Fei-Fei, L., Zitnick, C.L., Girshick, R.: · 2017
Closest in time.
Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: · 2025
Closest in time.