Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.Comment: NeurIPS 202

On the Automation and Diagnosis of Visual Intelligence

One of the ultimate goals of computer vision is to equip machines with visual intelligence: the ability to understand a scene at the level that is indistinguishable from human's. This not only requires detecting the 2D or 3D locations of objects, but also recognizing their semantic categories, or even higher level interactions. Thanks to decades of vision research as well as recent developments in deep learning, we are closer to this goal than ever. But to keep closing the gap, more research is needed on two themes. One, current models are still far from perfect, so we need a mechanism to keep proposing new, better models to improve performance. Two, while we are pushing for performance, it is also important to do careful analysis and diagnosis of existing models, to make sure we are indeed moving in the right direction. In this dissertation, I study either of the two research themes for various steps in the visual intelligence pipeline. The first part of the dissertation focuses on category-level understanding of 2D images, which is arguably the most critical step in the visual intelligence pipeline as it bridges vision and language. The theme is on automating the process of model improvement: in particular, the architecture of neural networks. The second part extends the visual intelligence pipeline along the language side, and focuses on the more challenging language-level understanding of 2D images. The theme also shifts to diagnosis, by examining existing models, proposing interpretable models, or building diagnostic datasets. The third part continues in the diagnosis theme, this time extending along the vision side, focusing on how incorporating 3D scene knowledge may facilitate the evaluation of image recognition models