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In this paper, we propose a simple and general framework for training very tiny CNNs for object detection.
Distinctive image features from scale-invariant keypoints
Lowe, D.G.: · 2004
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An hog-lbp human detector with partial occlusion handling
Wang, X., Han, T.X., Yan, S.: · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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The fastest deformable part model for object detection
Yan, J., Lei, Z., Wen, L., Li, S.Z.: · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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Object detection by labeling superpixels
Yan, J., Yu, Y., Zhu, X., Lei, Z., Li, S.Z.: · 2015
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Deepid-net: Deformable deep convolutional neural networks for object detection
Ouyang, W., Wang, X., Zeng, X., Qiu, S., Luo, P., Tian, Y., Li, H., Yang, S., Wang, Z., Loy, C.C., et al.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
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Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., David, J.P.: · 2015
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., Dally, W.: · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J.: · 2015
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Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: · 2016
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: · 2016
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., Sun, J.: · 2017
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Mimicking very efficient network for object detection
Li, Q., Jin, S., Yan, J.: · 2017
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Crafting gbd-net for object detection
Zeng, X., Ouyang, W., Yan, J., Li, H., Xiao, T., Wang, K., Liu, Y., Zhou, Y., Yang, B., Wang, Z., et al.: · 2017
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Recurrent scale approximation for object detection in cnn
Liu, Y., Li, H., Yan, J., Wei, F., Wang, X., Tang, X.: · 2017
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Chained cascade network for object detection
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Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., Zou, Y.: · 2016
Cited alongside, same era.
Learning the number of neurons in deep networks
Alvarez, J.M., Salzmann, M.: · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: · 2016
Cited alongside, same era.
Compact deep convolutional neural networks with coarse pruning
Anwar, S., Sung, W.: · 2016
Cited alongside, same era.
Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., Chen, Y.: · 2016
Cited alongside, same era.
Eie: efficient inference engine on compressed deep neural network
Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A., Dally, W.J.: · 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.
Ouyang, W., Wang, K., Zhu, X., Wang, X.: · 2017
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Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2017
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Interleaved group convolutions for deep neural networks
Zhang, T., Qi, G.J., Xiao, B., Wang, J.: · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., Sun, J.: · 2017
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Local binary convolutional neural networks
Juefei-Xu, F., Boddeti, V.N., Savvides, M.: · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: · 2017
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: · 2017
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Pruning convolutional neural networks for resource efficient inference
Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: · 2017
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Designing energy-efficient convolutional neural networks using energy-aware pruning
Yang, T.J., Chen, Y.H., Sze, V.: · 2017
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