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We present a method for generating colored 3D shapes from natural language.
Cumulated gain-based evaluation of IR techniques
Järvelin, K., Kekäläinen, J.: · 2002
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Simplification and repair of polygonal models using volumetric techniques
Nooruddin, F.S., Turk, G.: · 2003
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Canonical correlation analysis: An overview with application to learning methods
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: · 2004
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., LeCun, Y.: · 2005
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Distance metric learning for large margin nearest neighbor classification
Weinberger, K.Q., Blitzer, J., Saul, L.K.: · 2006
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Multimodal deep learning
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: · 2011
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Multimodal learning with deep Boltzmann machines
Srivastava, N., Salakhutdinov, R.R.: · 2012
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Deep canonical correlation analysis
Andrew, G., Arora, R., Bilmes, J., Livescu, K.: · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: · 2013
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Improving image-sentence embeddings using large weakly annotated photo collections
Gong, Y., Wang, L., Hodosh, M., Hockenmaier, J., Lazebnik, S.: · 2014
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Unifying visual-semantic embeddings with multimodal neural language models
Kiros, R., Salakhutdinov, R., Zemel, R.S.: · 2014
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Improved multimodal deep learning with variation of information
Sohn, K., Shang, W., Lee, H.: · 2014
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A multi-view embedding space for modeling internet images, tags, and their semantics
Gong, Y., Ke, Q., Isard, M., Lazebnik, S.: · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
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Conditional generative adversarial nets
Mirza, M., Osindero, S.: · 2014
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Associating neural word embeddings with deep image representations using Fisher vectors
Klein, B., Lev, G., Sadeh, G., Wolf, L.: · 2015
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Joint embeddings of shapes and images via CNN image purification
Li, Y., Su, H., Qi, C.R., Fish, N., Cohen-Or, D., Guibas, L.J.: · 2015
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ShapeNet: An information-rich 3D model repository
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: · 2015
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Finding linear structure in large datasets with scalable canonical correlation analysis
Ma, Z., Lu, Y., Foster, D.: · 2015
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FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., Philbin, J.: · 2015
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Sketch-based 3D shape retrieval using convolutional neural networks
Wang, F., Kang, L., Li, Y.: · 2015
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Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Tobias Springenberg, J., Brox, T.: · 2015
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Learning a predictable and generative vector representation for objects
Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: · 2016
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Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B.: · 2016
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spaCy: Industrial-strength NLP
Honnibal, M., Montani, I.: · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
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StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., Metaxas, D.: · 2017
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Learning by association-a versatile semi-supervised training method for neural networks
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Radford, A., Metz, L., Chintala, S.: · 2015
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Learning deep representations of fine-grained visual descriptions
Reed, S., Akata, Z., Lee, H., Schiele, B.: · 2016
Cited alongside, same era.
Learning deep structure-preserving image-text embeddings
Wang, L., Li, Y., Lazebnik, S.: · 2016
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Generating interpretable images with controllable structure
Reed, S., van den Oord, A., Kalchbrenner, N., Bapst, V., Botvinick, M., de Freitas, N.: · 2016
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Learning what and where to draw
Reed, S., Akata, Z., Mohan, S., Tenka, S., Schiele, B., Lee, H.: · 2016
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Generative adversarial text-to-image synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: · 2016
Cited alongside, same era.
Deep metric learning via lifted structured feature embedding
Song, H.O., Xiang, Y., Jegelka, S., Savarese, S.: · 2016
Cited alongside, same era.
Haeusser, P., Mordvintsev, A., Cremers, D.: · 2017
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Learning robust visual-semantic embeddings
Tsai, Y.H.H., Huang, L.K., Salakhutdinov, R.: · 2017
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Beyond instance-level image retrieval: Leveraging captions to learn a global visual representation for semantic retrieval
Gordo, A., Larlus, D.: · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs
Tatarchenko, M., Dosovitskiy, A., Brox, T.: · 2017
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Interactive 3d modeling with a generative adversarial network
Liu, J., Yu, F., Funkhouser, T.: · 2017
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Grass: Generative recursive autoencoders for shape structures
Li, J., Xu, K., Chaudhuri, S., Yumer, E., Zhang, H., Guibas, L.: · 2017
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Arjovsky, M., Chintala, S., Bottou, L.: · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
Tulsiani, S., Zhou, T., Efros, A.A., Malik, J.: · 2017
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Haeusser, P., Frerix, T., Mordvintsev, A., Cremers, D.: · 2017
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: · 2017
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Deep metric learning with angular loss
Wang, J., Zhou, F., Wen, S., Liu, X., Lin, Y.: · 2017
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