UC San Diego

Large-scale datasets play a key role in the success of modern computer vision models. However, there are limitations of current datasets due to the data collection strategy. When an object has a canonical pose, pose bias is usually common. And models can only perform good on specific poses. We mitigate this by introducing two data augmentation methods, one in feature space with feature transfer, the other in image space with novel view synthesis. Both of them can improve the pose diversity of the current dataset. Imbalance class distribution is the nature of real world. To deal with this problem, we first discuss the few-shot open-set problem. By introducing meta-learning to open-set detection, PEELER rejects unseen samples with no cost of seen class accuracy. With a larger class label space, long-tailed recognition problem is then discussed. GistNet improve the long-tailed performance by geometry transfer, while Breadcrumb mitigates the over-fitting of few-shot samples by feature back-tracking. Last but not least, the task of semi-supervised long-tailed recognition is introduced. Alternate learning is presented to combine semi-supervised learning with long-tailed recognition. All of above mentioned methods are proved to be useful when facing certain limitations of computer vision datasets.