Extracting behavioral measurements non-invasively from video is stymied by the fact that it is
a hard computational problem. Recent advances in deep learning have tremendously …

To date, most existing self-supervised learning methods are designed and optimized for
image classification. These pre-trained models can be sub-optimal for dense prediction …

Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet
pre-training is commonly used to initialize the backbones of object detection and …

Despite the fast progress in training specialized models for various tasks, learning a single
general model that works well for many tasks is still challenging for computer vision. Here …

In many real-world deployments of machine learning systems, data arrive piecemeal. These
learning scenarios may be passive, where data arrive incrementally due to structural …

Neural networks are highly effective tools for pose estimation. However, as in other
computer vision tasks, robustness to out-of-domain data remains a challenge, especially for …

Deep learning based visual cognition has greatly improved the accuracy of defect detection,
reducing processing times and increasing product throughput across a variety of …

Transfer learning with pre-training on large-scale datasets has played an increasingly
significant role in computer vision and natural language processing recently. However, as …

Transfer learning has proven to be a successful technique to train deep learning models in
the domains where little training data is available. The dominant approach is to pretrain a …

Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily
available supervision of natural language. It improves the performance of downstream vision …