3 accepted papers
Visual document understanding (VDU) is a challenging task that involves understanding documents across various modalities (text and image) and layouts (forms, tables, etc.). This study aims to enhance generalizability of small VDU models by distilling knowledge from LLMs. We identify that directly p…
We present a new formulation for structured information extraction (SIE) from visually rich documents. We address the limitations of existing IOB tagging and graph-based formulations, which are either overly reliant on the correct ordering of input text or struggle with decoding a complex graph. Ins…
In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framew…