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Found 11 papers, 8 papers with code
Order-Embeddings of Images and Language
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images.
Ranked #88 on
Natural Language Inference
on SNLI
Action Recognition using Visual Attention
We propose a soft attention based model for the task of action recognition in videos.
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.
Skip-Thought Vectors
The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice.
Ranked #2 on
Semantic Similarity
on SICK
Exploring Models and Data for Image Question Answering
A suite of baseline results on this new dataset are also presented.
Ranked #5 on
Video Question Answering
on SUTD-TrafficQA
Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Ranked #156 on
Image Classification
on CIFAR-10
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings.
Training Neural Networks with Stochastic Hessian-Free Optimization
Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks.
Deep Representations and Codes for Image Auto-Annotation
The task of assigning a set of relevant tags to an image is challenging due to the size and variability of tag vocabularies.
