Topic Tag: image captioning

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 Cold-Start Reinforcement Learning with Softmax Policy Gradients

  

Policy-gradient approaches to reinforcement learning have two common and undesirable overhead procedures, namely warm-start training and sample variance reduction. In this paper, we describe a reinforcement learning method based on a softmax policy that requires neither of these procedures. Our met…


 Long Text Generation via Adversarial Training with Leaked Information

     

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the gener…


 Self-Guiding Multimodal LSTM – when we do not have a perfect training dataset for image captioning

     

In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning model is proposed to handle uncontrolled imbalanced real-world image-sentence dataset. We collect FlickrNYC dataset from Flickr as our testbed with 306,165 images and the original text descriptions uploaded by the users are ut…


 What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?

   

In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary generation' component. This view suggests that the image features should beinjected’ into the RNN. This is in fact the dominant view in the literature. Alternatively, the RNN can inste…


 Learning the Enigma with Recurrent Neural Networks

    

Recurrent neural networks (RNNs) represent the state of the art in translation, image captioning, and speech recognition. They are also capable of learning algorithmic tasks such as long addition, copying, and sorting from a set of training examples. We demonstrate that RNNs can learn decryption al…