Although an object may appear in numerous contexts, we often describe it in a limited
number of ways. Language allows us to abstract away visual variation to represent and …

Recent self-supervised contrastive learning methods greatly benefit from the Siamese
structure that aims at minimizing distances between positive pairs. For high performance …

The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled
data to solve complex tasks. While there has been excellent progress with simple, image …

Although self-/un-supervised methods have led to rapid progress in visual representation
learning, these methods generally treat objects and scenes using the same lens. In this …

Dense retrievers have achieved impressive performance, but their demand for abundant
training data limits their application scenarios. Contrastive pre-training, which constructs …

Although supervised learning has been highly successful in improving the state-of-the-art in
the domain of image-based computer vision in the past, the margin of improvement has …

General target detection with deep learning has made tremendous strides in the past few
years. However, small target detection sometimes is associated with insufficient sample size …

Deep learning (DL) methods have been widely studied and applied in the field of computer
vision (CV) over the past decades. The biggest disadvantage of classic DL methods is that …

Most self-supervised learning (SSL) methods often work on curated datasets where the
object-centric assumption holds. This assumption breaks down in uncurated images …

While self-supervised learning has been shown to benefit a number of vision tasks, existing
techniques mainly focus on image-level manipulation, which may not generalize well to …