Caption Generation
89 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Caption Generation
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Libraries
Use these libraries to find Caption Generation models and implementations
Concadia
Most implemented papers
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning
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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.
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
adityac94/Grad_CAM_plus_plus
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Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
Recurrent Neural Network Regularization
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.
Microsoft COCO Captions: Data Collection and Evaluation Server
In this paper we describe the Microsoft COCO Caption dataset and evaluation server.
Where to put the Image in an Image Caption Generator
When a recurrent neural network language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN -- conditioning the language model by `injecting' image features -- or in a layer following the RNN -- conditioning the language model by `merging' image features.
Scalable Bayesian Optimization Using Deep Neural Networks
automl/pybnn
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Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Sequence to Sequence -- Video to Text
nasib-ullah/video-captioning-models-in-Pytorch
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Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip.
An Actor-Critic Algorithm for Sequence Prediction
We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL).
Deep Reinforcement Learning For Sequence to Sequence Models
In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories.
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training.
