Humans observe the world through a combination of different modalities, like vision, hearing, and our understanding of language. Machines, on the other hand, interpret the world through data that algorithms can process.

So, when a machine “sees” a photo, it must encode that photo into data it can use to perform a task like image classification. This process becomes more complicated when inputs come in multiple formats, like videos, audio clips, and images.

“The main challenge here is, how can a machine align those different modalities? As humans, this is easy for us. We see a car and then hear the sound of a car driving by, and we know these are the same thing. But for machine learning, it is not that straightforward,” says Alexander Liu, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author of a paper tackling this problem. 

Liu and his collaborators developed an artificial intelligence technique that learns to represent data in a way that captures concepts which are shared between visual and audio modalities. For instance, their method can learn that the action of a baby crying in a video is related to the spoken word “crying” in an audio clip.

Using this knowledge, their machine-learning model can identify where a certain action is taking place in a video and label it.

It performs better than other machine-learning methods at cross-modal retrieval tasks, which involve finding a piece of data, like a video, that matches a user’s query given in another form, like spoken language. Their model also makes it easier for users to see why the machine thinks the video it retrieved matches their query.

This technique could someday be utilized to help robots learn about concepts in the world through perception, more like the way humans do.

Joining Liu on the paper are CSAIL postdoc SouYoung Jin; grad students Cheng-I Jeff Lai and Andrew Rouditchenko; Aude Oliva, senior research scientist in CSAIL and MIT director of the MIT-IBM Watson AI Lab; and senior author James Glass, senior research scientist and head of the Spoken Language Systems Group in CSAIL. The research will be presented at the Annual Meeting of the Association for Computational Linguistics.

A better retriever

They tested the model on cross-modal retrieval tasks using three datasets: a video-text dataset with video clips and text captions, a video-audio dataset with video clips and spoken audio captions, and an image-audio dataset with images and spoken audio captions.

For example, in the video-audio dataset, the model chose 1,000 words to represent the actions in the videos. Then, when the researchers fed it audio queries, the model tried to find the clip that best matched those spoken words.

“Just like a Google search, you type in some text and the machine tries to tell you the most relevant things you are searching for. Only we do this in the vector space,” Liu says.

Not only was their technique more likely to find better matches than the models they compared it to, it is also easier to understand.

Because the model could only use 1,000 total words to label vectors, a user can more see easily which words the machine used to conclude that the video and spoken words are similar. This could make the model easier to apply in real-world situations where it is vital that users understand how it makes decisions, Liu says.

The model still has some limitations they hope to address in future work. For one, their research focused on data from two modalities at a time, but in the real world humans encounter many data modalities simultaneously, Liu says.

“And we know 1,000 words works on this kind of dataset, but we don’t know if it can be generalized to a real-world problem,” he adds.

Plus, the images and videos in their datasets contained simple objects or straightforward actions; real-world data are much messier. They also want to determine how well their method scales up when there is a wider diversity of inputs.

This research was supported, in part, by the MIT-IBM Watson AI Lab and its member companies, Nexplore and Woodside, and by the MIT Lincoln Laboratory.