456 citations · 700 across the 23 of their papers we have counts for
24 papers
R-VLM: Region-Aware Vision Language Model for Precise GUI Grounding
Joonhyung Park, Peng Tang, Sagnik Das +4
Visual agent models for automating human activities on Graphical User Interfaces (GUIs) have emerged as a promising research direction, driven by advances in large Vision Language…
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
Ofir Abramovich, Niv Nayman, Sharon Fogel +7
In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language mo…
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models
Peng Tang, Pengkai Zhu, Tian Li +3
Encoder-decoder transformer models have achieved great success on various vision-language (VL) tasks, but they suffer from high inference latency. Typically, the decoder takes up m…
Multiple-Question Multiple-Answer Text-VQA
Peng Tang, Srikar Appalaraju, R. Manmatha +2
We present Multiple-Question Multiple-Answer (MQMA), a novel approach to do text-VQA in encoder-decoder transformer models. The text-VQA task requires a model to answer a question…
DocFormerv2: Local Features for Document Understanding
Srikar Appalaraju, Peng Tang, Qi Dong +3
We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU). The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extrac…
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
Jiang Liu, Hui Ding, Zhaowei Cai +4
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…