Decoding Neural Image Captioning

Jul 2017

24 Mon

25 Tue

26 Wed

27 Thu

28 Fri

29 Sat 09:00 AM – 05:40 PM IST

30 Sun

Make a submission

MLR Whitefield

##About AnthillInside:
In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. Anthill Inside is the new avataar of the Deep Learning conference.
Anthill Inside attempts to bridge the gap bringing theoretical advances closer to functioning reality.
Proposals are invited for full length talks, crisp talks and poster/demo sessions in the area of ML+DL. The talks need to focus on the techniques used, and may be presented independent of the domain wherein they are applied.
We also invite talks on novel applications of ML+DL, and methods of realising the same in hardware/software.
Case studies of how DL and ML have been applied in different domains will continue to be discussed at The Fifth Elephant.

https://anthillinside.in/2017/

Topics: we are looking for talks covering the following: #

Machine Learning with end-to-end application
Deep Learning
Artificial Intelligence
Hardware / software implementations of advanced Machine Learning and Deep Learning
IoT and Deep Learning
Operations research and Machine Learning

##Format:
Anthill Inside is a two-track conference:

Talks in the main auditorium and hall 2.
Birds of Feather (BOF) sessions in expo area.

We are inviting proposals for:

Full-length 40-minute talks.
Crisp 15-minute how-to talks or introduction to a new technology.
Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
Hands-on workshop sessions of 3 and 6 hour duration where participants follow instructors on their laptops.
Birds of Feather (BOF) sessions.

You must submit the following details along with your proposal, or within 10 days of submission:

Draft slides, mind map or a textual description detailing the structure and content of your talk.
Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant or last year at Deep Learning.
If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.

##Selection Process:

Proposals will be filtered and shortlisted by an Editorial Panel.
Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
Proposers are also encouraged to vote and comment on other proposals submitted here.

We expect you to submit an outline of your proposed talk, either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal to start evaluating your proposal.

Selection Process Flowchart

You can check back on this page for the status of your proposal. We will notify you if we either move your proposal to the next round or if we reject it. Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

A speaker is NOT confirmed a slot unless we explicitly mention so in an email or over any other medium of communication.

There is only one speaker per session. Entry is free for selected speakers.

We might contact you to ask if you’d like to repost your content on the official conference blog.

##Travel Grants:

Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.

##Commitment to Open Source:

We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.

##Important Dates:

Deadline for submitting proposals: July 10
First draft of the coference schedule: July 15
Tutorial and workshop announcements: June 30
Final conference schedule: July 20
Conference date: July 30

##Contact:

For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

Please note, we will not evaluate proposals that do not have a slide deck and a video in them.

Hosted by

Anthill Inside

Anthill Inside is a forum for conversations about Artificial Intelligence and Deep Learning, including: Tools Techniques Approaches for integrating AI and Deep Learning in products and businesses. Engineering for AI. more

All submissions

Previous Next

Submitted Jun 10, 2017

Section: Full talk Technical level: Intermediate

Humans have been captioning images involuntary since decades and now in the age of social media where every image have a caption over various social platforms. Psychologically those things are affected by events and scenarios running in mind or infulenced by nearby activities and emotion. Sometimes those are far-far away from real context. Describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing.

This talk will cover some of the common deep learning architectures, latest state-of-the-art pre-trained models for image captioning, describe advantages and concerns, and provide hands-on experience.

This talk shall be beneficial for those who are interested in the advance applications of Deep Neural Networks and what can be achieved with the combination of different state-of-the-art models. We also aim to provide an open source implementation in Keras, a higher abstraction library which uses Theano/TensorFlow/CNTK as backend for writing Deep Neural Networks (DNN) over CPU and GPU.

Outline #

In this talk, I will present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on COCO dataset shows the accuracy of the model and the fluency of the language it learns solely from image descriptions.

Learning a model like this would be incredible. It would be a great way to relate how relevant an image and caption are to each other. For a batch of images and captions, we can use the model to map them all into this embedding space, compute a distance metric, and for each image and for each caption find its nearest neighbors.

I will cover the following:

What is deep learning, CNN and RNN?
Motivation: CNN and RNN real world applications, state-of-the-art results
Internal structure of Vanilla models
Description of dataset and introduction to word2vec
Deep dive into word embeddings, image encoder, caption encoder, cost function.
Impact of GPUs (Some practical thoughts on hardware and software)
Explaination of Code

Requirements #

Basic knowledge of DeepLearning, MLP, Backpropagation, CNN, RNN, pre-trained models such as VGG and lot of enthusiasm.

Speaker bio #

Sachin Kumar is currently second year undergraduate pursuing Bachelor of Engineering in Information Technology at Netaji Subhash Institute of Technology(NSIT), New Delhi. He is also Teaching Assistant in Machine Learning Course at Coding Blocks. His interests includes Machine Learning, Artificial Intelligence, Deep Learning and Evolutionary Computing.