Research in machine perception tackles the hard problems of understanding images, sounds, music and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as: content-based search in Google Photos and Image Search, natural handwriting interfaces for Android, optical character recognition for Google Drive documents, and recommendation systems that understand music and YouTube videos. Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture known as Inception that achieves state-of-the-art results on academic benchmarks and allows users to easily search through their large collection of Google Photos. The ability to mine meaningful information from multimedia is broadly applied throughout Google.
Edges to Shapes to Concepts: Adversarial Augmentation for Robust Vision
Computer Vision and Pattern Recognition (CVPR) 2023 (2023) (to appear)
Contextualized Spatial-Temporal Contrastive Learning with Self-Supervision
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 13977-13986
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
European Conference on Computer Vision (ECCV) (2022), pp. 249-266
Dynamic Pre-training of Vision-Language Models
ICLR 2023 Workshop on Multimodal Representation Learning (2023)
Some of our teams
Our researchers work across the world
Together, our research teams tackle tough problems.