5 accepted papers
Current self-supervised methods can primarily be categorized into contrastive learning and masked image modeling. Extensive studies have demonstrated that combining these two approaches can achieve state-of-the-art performance. However these methods essentially reinforce the global consistency of co…
Transformer has demonstrated exceptional performance on a variety of vision tasks. However, its high computational complexity can become problematic. In this paper, we conduct a systematic analysis of the complexity of each component in vision transformers, and identify an easily overlooked detail:…
Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensional outputs in object-level visual tasks. In this paper, we propose an object-centric vision framework, Obj2Seq. Obj2Se…
Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of…
Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) and achieved great success. However, it has not been fully explored in visual self-supervised learning. Meanwhile, previous methods only consider the high-level feature and learning representation…