11 citations · 12 across the 3 of their papers we have counts for
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cs.CV2022★ 11 cited
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework
Shu Zhang, Ran Xu, Caiming Xiong +1
Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In…
cs.CV2021★ 1 cited
Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks
Le Xue, Mingfei Gao, Zeyuan Chen +2
We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks. We introduce 14 novel form transformations to evaluate…
cs.CV2020
Proposal Learning for Semi-Supervised Object Detection
Peng Tang, Chetan Ramaiah, Yan Wang +2
In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled…