To generate theĬontextual information, the softmaxed scores and the encoder hidden states are then combined toįormulate a vector representation. These score are then combined and softmax is applied on them. The alignment score is being calculated for each hidden state of the encoder and the decoder’s previous Hidden states, i,e encoder will produce hidden states for all the images in the input sequences. We are using the Local attention based architecture for our model. Padded all the sequence to be the same length as the longest one.Tokenized the captions (for example, by splitting on spaces) to obtain a vocabulary of unique.Resized images to 224 X 224 followed by pixel normalization to suit our VGG16 image.Added start and end tags for every caption, so that model understands the start and end of.Cleaned the captions by removing punctuations, single characters, and numeric values.We conducted following data Preprocessing steps.The datasetĬontains a total of 8092 images each with 5 captions, in total we have 40460 properly labelled We leveraged Flickr 8K dataset consisting 5 captions for each image to train our model. Moreover, the above semantic knowledge has toīe expressed in a natural language like English, which means that a language model is needed inĪddition to visual understanding. One of the key challenge involves generating description that must capture not only the objectsĬontained in an image, but also express how these objects relate to each other as well as theirĪttributes and the activities they are involved in.
Helping visually impaired people better understand the content of images on the web. That connects computer vision and natural language processing. Problem StatementĪutomatically describing the content of an image is a fundamental problem in artificial intelligence The model architecture is inspired by the Show, Attend and Tell paper, where an attentionīased model was introduced to automatically learn and describe the content of images. LSTM then generates the next word and also passes on the information to the new hidden state hi+1 Will select the relevant part of the image using the attention mechanism which will be zi (whichĬaptures only relevant information from the image) and this will be go as an input to the LSTM. While predicting the next word while generating captionsįor an image, if we have previously predicted i words, the hidden state will be hi. Layer makes the model as attention model. The decoder will only use specific parts of the image rather thanĬonditioning on the entire hidden state h produced from the convolutional neural network.We can understand that there is one additional layer from the classic architecture, and this new The attention mechanism will help the decoder to focus We are trying to encounter the problem faced by the "classic" image captioning method using theĪttention mechanism in the decoder.
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