NVIDIA's Shalini De Mello Talks NeurIPS Successes

Shalini De Mello, a principal research scientist at NVIDIA who’s made her mark inventing computer vision technology that contributes to driver safety, finished 2020 with a bang — presenting two posters at the prestigious NeurIPS conference in December.

A 10-year NVIDIA veteran, De Mello works on self-supervised and few-shot learning, 3D reconstruction, viewpoint estimation and human-computer interaction.

She told NVIDIA AI Podcast host Noah Kravitz about her NeurIPS submissions on reconstructing 3D meshes and self-learning transformations for improving head and gaze redirection — both significant challenges for computer vision.

The AI Podcast · NVIDIA’s Shalini De Mello Talks Self-Supervised AI, NeurIPS Successes – Ep. 140

De Mello’s first poster demonstrates how she and her team successfully manage to recreate 3D models in motion without requiring annotations of 3D mesh, 2D keypoints or camera pose — even on such kinetic figures as animals in the wild.

The second poster takes on the issue of datasets in which large portions are unlabeled — focusing specifically on datasets consisting of images of human faces with many variables, including lighting, reflections and head and gaze orientation. De Mello achieved an architecture that could self-learn these variations and control them.

De Mello intends to continue focusing on creating self-supervising AI systems that require less data to achieve the same quality output, which she envisions ultimately helping to reduce bias in AI algorithms.

Key Points From This Episode:

Early in her career at NVIDIA, De Mello noticed that technologies for looking inside the car cabin weren’t as mature as the algorithms for automotive vision outside the car. She focused her research on the former, leading to the creation of NVIDIA’s DRIVE IX product for AI-based automotive interfaces in cars.
While science has been a lifelong passion, De Mello discovered an appreciation for art and found the perfect blend of the two in signal and image processing. She could immediately see the effects of AI on visual content.

Tweetables:

“We as humans are able to learn effectively with less data — how can we make learning systems do the same? This is a fundamental question to answer for the viability of AI” [29:29]

“Looking back at my career, the one thing I’ve learned is that it’s really important to follow your passion” [32:37]

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