Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers

arXiv:2403.06601v1 Announce Type: new
Abstract: Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model. Due to the complexity of this task, large training datasets are rare in many domains, which makes the training of large networks challenging. This data sparsity necessitates the establishment of pre-training strategies akin to the state-of-the-art in computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension transfer learning for image-to-graph transformers. …

abstract arxiv complexity cs.ai cs.cv data datasets detection domain domains graph image networks object prediction relationship sparsity training training datasets transformation transformers type