ΑΙhub.org
 

New algorithm follows human intuition to make visual captioning more grounded

visual captions | AIhub

Annotating and labeling datasets for machine learning problems is an expensive and time-consuming process for computer vision and natural language scientists. However, a new deep learning approach is being used to decode, localize, and reconstruct image and video captions in seconds, making the machine-generated captions more reliable and trustworthy.

To solve this problem, researchers at the Machine Learning Center at Georgia Tech (ML@GT) and Facebook have created the first cyclical algorithm that can be applied to visual captioning models. The model is able to use the three-step processing during training to make the model more visually-grounded without human annotations or introducing additional computations when deployed, saving researchers time and money on their datasets.

The algorithm employs attention mechanisms, an intuitive concept for humans, when looking at a photo or video. This means that it tries to determine what aspects are important in an image and sequentially create a sentence explaining the visual.

This new model helps solve issues with previous attempts where an algorithm would make its decision based on prior linguistic biases instead of what it is actually “seeing.” This would lead to algorithms having what researchers refer to as object hallucinations. Object hallucinations occur when an algorithmic model assumes an object like a table is in a photo because in previous images, someone with a laptop was always sitting at a table. In this instance, the model is unable to understand a situation where a person has a laptop on their lap instead of a table. This new model helps alleviate the object hallucination problem, thus making the model more reliable and trustworthy.

Chih-Yao MaChih-Yao Ma, a Ph.D. student in the School of Electrical and Computer Engineering, envisions this model being used in situations like describing what happens in the scene as a technology to assist people who are visually impaired to overcome their real daily visual challenges. The model would be a good fit in such instances, because it can alleviate the linguistic bias and object hallucination issues in existing visual captioning models.

This work has been accepted to the European Conference on Computer Vision (ECCV), which takes place virtually August 23-28, 2020.

For more information on ML@GT at ECCV, visit our conference website.

Read the paper in full

Learning to Generate Grounded Visual Captions without Localization Supervision
Chih-Yao Ma, Yannis Kalantidis, Ghassan AlRegib, Peter Vajda, Marcus Rohrbach, Zsolt Kira
Georgia Tech, NAVER LABS Europe, Facebook




Allie McFadden is the communications officer for the Machine Learning Center at Georgia Tech and the Constellations Center for Equity in Computing at Georgia Tech.
Allie McFadden is the communications officer for the Machine Learning Center at Georgia Tech and the Constellations Center for Equity in Computing at Georgia Tech.

Machine Learning Center at Georgia Tech




            AIhub is supported by:


Related posts :



#AAAI2024 invited talk: Milind Tambe – using ML for social good

Winner of the 2024 AAAI Award for Artificial Intelligence for the Benefit of Humanity, Milind spoke about recent projects.
01 March 2024, by

AIhub monthly digest: February 2024 – causal relations in text, applied reinforcement learning, and AAAI 2024

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
29 February 2024, by

#AAAI2024 in tweets: part two

Find out what the conference participants got up to during the second half of the event.
28 February 2024, by

Unlocking the potential of entity-centric knowledge graphs: transforming healthcare and beyond

The concept of entity-centric knowledge graphs holds promise in reshaping how we organize, access, and leverage data.
27 February 2024, by and

Congratulations to the #AAAI2024 outstanding paper winners

The winners of the outstanding papers were announced at the conference during the opening ceremony.
26 February 2024, by

#AAAI2024 in tweets: part one

Find out what the conference participants have been up to over the past few days.
23 February 2024, by





©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association