Search Results for author: Mingfei Gao

Found 15 papers, 2 papers with code

Field Extraction from Forms with Unlabeled Data

no code implementations8 Oct 2021 Mingfei Gao, Zeyuan Chen, Nikhil Naik, Kazuma Hashimoto, Caiming Xiong, ran Xu

We propose a novel framework to conduct field extraction from forms with unlabeled data.

Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks

no code implementations8 Oct 2021 Le Xue, Mingfei Gao, Zeyuan Chen, Caiming Xiong, ran Xu

We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks.

Optical Character Recognition

Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation

1 code implementation ICCV 2021 Luyu Yang, Yan Wang, Mingfei Gao, Abhinav Shrivastava, Kilian Q. Weinberger, Wei-Lun Chao, Ser-Nam Lim

To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.

Unsupervised Domain Adaptation

InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

no code implementations ECCV 2020 Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis

Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9. 0% mAP on the nuScenes test set.

3D Object Detection Autonomous Driving +1

WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos

no code implementations CVPR 2021 Mingfei Gao, Yingbo Zhou, ran Xu, Richard Socher, Caiming Xiong

Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications.

Action Detection Action Recognition

WSLLN:Weakly Supervised Natural Language Localization Networks

no code implementations IJCNLP 2019 Mingfei Gao, Larry Davis, Richard Socher, Caiming Xiong

We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.

Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost

no code implementations ECCV 2020 Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan O. Arik, Larry S. Davis, Tomas Pfister

Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance.

Active Learning Image Classification +1

WSLLN: Weakly Supervised Natural Language Localization Networks

no code implementations31 Aug 2019 Mingfei Gao, Larry S. Davis, Richard Socher, Caiming Xiong

We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries.

Goal-oriented Object Importance Estimation in On-road Driving Videos

no code implementations8 May 2019 Mingfei Gao, Ashish Tawari, Sujitha Martin

We propose a novel framework that incorporates both visual model and goal representation to conduct OIE.

StartNet: Online Detection of Action Start in Untrimmed Videos

no code implementations ICCV 2019 Mingfei Gao, Mingze Xu, Larry S. Davis, Richard Socher, Caiming Xiong

We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos.

Action Classification Policy Gradient Methods

Temporal Recurrent Networks for Online Action Detection

2 code implementations ICCV 2019 Mingze Xu, Mingfei Gao, Yi-Ting Chen, Larry S. Davis, David J. Crandall

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed.

Action Detection

NISP: Pruning Networks using Neuron Importance Score Propagation

no code implementations CVPR 2018 Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I. Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, Larry S. Davis

In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification, for a pruned network to retrain its predictive power.

Network Pruning

C-WSL: Count-guided Weakly Supervised Localization

no code implementations ECCV 2018 Mingfei Gao, Ang Li, Ruichi Yu, Vlad I. Morariu, Larry S. Davis

We introduce count-guided weakly supervised localization (C-WSL), an approach that uses per-class object count as a new form of supervision to improve weakly supervised localization (WSL).

Dynamic Zoom-in Network for Fast Object Detection in Large Images

no code implementations CVPR 2018 Mingfei Gao, Ruichi Yu, Ang Li, Vlad I. Morariu, Larry S. Davis

We introduce a generic framework that reduces the computational cost of object detection while retaining accuracy for scenarios where objects with varied sizes appear in high resolution images.

Real-Time Object Detection

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