Learning Generative models with Visual attentions in the field of Image Captioning
Image caption generation is the task of generating a descriptive and appropriate sentence of a given image. For humans, the task looks straightforward with the motive of summarising the image in a single sentence incorporating the interactions between the various components present in the image. But to replicate this phenomenon in an artificial framework is a very challenging task. Attention fixes this problem as it allows the network to look over the relevant features of the encoder as an input to the decoder at each time step. In this session, we show how attention mechanism enhances the performance of language translation tasks in an encoder-decoder framework.
Before the attention mechanism in sequence to sequence settings, the entire sequence was encoded into a thought/context vector which was used to initialize the decoder to generate the output sequence. But the major shortcoming of this methodology was that no weightage was given to the encoder features in the context of the generated sequence, thereby confounding the network and resulting in the inadequate output sequence.
Inspired by the outstanding results of using attention mechanisms in machine translation and other seq2seq tasks, there have been few advancements in the field of computer vision using attention techniques. In this session, we incorporate visual attention mechanisms in generating relevant captions from images using a deep learning framework.
Outline/Structure of the Talk
- Intro to Image Captioning - 2 mins
- Real-life use cases of image captioning - 2 mins
- History & Evolution of Image Captioning - 4 mins
- Sequence to Sequence Learning in NLP - 2 mins
- Show and Tell: Sequence to Sequence Learning in Vision - 3 mins
- Show Attend and Tell: Image Captioning with Attention - 5 mins
- Image Captioning demo - 2 mins
Learning Outcome
- A drill down on the various past approaches in Image captioning and intuitive reasons for their failure
- Understanding of receptive fields in CNN’s
- The motivation of using attention in an encoder-decoder based framework
- About different variations and intuitions of Attention mechanism in an encoder-decoder framework
Target Audience
Our Target Audience will be both Industry ML&DL practitioners and academicians research in the field of Computer vision and Captioning. Image captioning as a field has a huge application in Information retrieval, search engines, recommendation engines etc. and hence is a very special topic for the industrial ML/DL practitioners. Using Attention mechanism in this field has opened up a lot of research scopes and hence is an extremely fascinating topic for academicians and researchers.
Prerequisites for Attendees
Our session will cover the basics of Image captioning explaining the earlier methods as well as the attention mechanisms in detail with real-life examples and use cases. So, anybody with a basic understanding of Probability, Linear Algebra, Machine Learning, Computer Vision will be able to understand and gain from our session.
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schedule Submitted 3 years ago
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Recent Activities
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Call for Proposals CLOSED
Ended on Apr 1 '20 11:59 PM IST
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