activity
20212023
most citedTAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

7 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20233 cited

Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations

Vibashan VS, Ning Yu, Chen Xing +5

Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human…

cs.LG2023

Neighborhood-Regularized Self-Training for Learning with Few Labels

Ran Xu, Yue Yu, Hejie Cui +5

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been succ…

cs.CV20227 cited

TAG: Boosting Text-VQA via Text-aware Visual Question-answer Generation

Jun Wang, Mingfei Gao, Yuqian Hu +5

Text-VQA aims at answering questions that require understanding the textual cues in an image. Despite the great progress of existing Text-VQA methods, their performance suffers fro…

cs.CV20211 cited

Virtuoso: Video-based Intelligence for real-time tuning on SOCs

Jayoung Lee, PengCheng Wang, Ran Xu +5

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile…

cs.CV20213 cited

Value Retrieval with Arbitrary Queries for Form-like Documents

Mingfei Gao, Le Xue, Chetan Ramaiah +3

We propose value retrieval with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field…