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Found 13 papers, 9 papers with code
On the Transferability of Visually Grounded PCFGs
We consider a zero-shot transfer learning setting where a model is trained on the source domain and is directly applied to target domains, without any further training.
DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization
The voxel features and global features are complementary and are both leveraged by a compositional NeRF decoder for volume rendering.
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
Given a video, we replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet.
Ranked #6 on
Action Classification
on Kinetics-600
(using extra training data)
Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
In a difficult zero-shot setting with no paired audio-text data, our model demonstrates state-of-the-art zero-shot performance on the ESC50 and US8K audio classification tasks, and even surpasses the supervised state of the art for Clotho caption retrieval (with audio queries) by 2. 2\% R@1.
Neural Bi-Lexicalized PCFG Induction
Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction.
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols.
Unsupervised Natural Language Parsing (Introductory Tutorial)
Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations.
An Empirical Study of Compound PCFGs
Compound probabilistic context-free grammars (C-PCFGs) have recently established a new state of the art for unsupervised phrase-structure grammar induction.
Visually Grounded Compound PCFGs
In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings.
Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain
Nominal roles are not labeled in the training data, and the learning objective instead pushes the labeler to assign roles predictive of the arguments.
Language Style Transfer from Sentences with Arbitrary Unknown Styles
Then, by recombining the content with the target style, we decode a sentence aligned in the target domain.
Gaussian Mixture Latent Vector Grammars
We introduce Latent Vector Grammars (LVeGs), a new framework that extends latent variable grammars such that each nonterminal symbol is associated with a continuous vector space representing the set of (infinitely many) subtypes of the nonterminal.
Structured Attentions for Visual Question Answering
In this paper, we demonstrate the importance of encoding such relations by showing the limited effective receptive field of ResNet on two datasets, and propose to model the visual attention as a multivariate distribution over a grid-structured Conditional Random Field on image regions.
