Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences
arXiv:1811.02745
Abstract
A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels. Hence the method suffers from a lack of detailed geometry. To resolve this issue, we propose Y^2Seq2Seq, a view-based model, to learn cross-modal representations by joint reconstruction and prediction of view and word sequences. Specifically, the network architecture of Y^2Seq2Seq bridges the semantic meaning embedded in the two modalities by two coupled `Y' like sequence-to-sequence (Seq2Seq) structures. In addition, our novel hierarchical constraints further increase the discriminability of the cross-modal representations by employing more detailed discriminative information. Experimental results on cross-modal retrieval and 3D shape captioning show that Y^2Seq2Seq outperforms the state-of-the-art methods.
arXiv commentTo be pubilished at AAAI 2019
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
- Sequence to Sequence -- Video to Text
- Learning by Association - A versatile semi-supervised training method for neural networks
- Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
- Cross-Modal Retrieval with Implicit Concept Association