62 citations · 174 across the 19 of their papers we have counts for
19 papers
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…
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
Le Xue, Mingfei Gao, Chen Xing +6
The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D coun…
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…
Burn After Reading: Online Adaptation for Cross-domain Streaming Data
Luyu Yang, Mingfei Gao, Zeyuan Chen +3
In the context of online privacy, many methods propose complex privacy and security preserving measures to protect sensitive data. In this paper, we argue that: not storing any sen…
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…
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
Mingfei Gao, Chen Xing, Juan Carlos Niebles +4
Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annota…
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…
Field Extraction from Forms with Unlabeled Data
Mingfei Gao, Zeyuan Chen, Nikhil Naik +3
We propose a novel framework to conduct field extraction from forms with unlabeled data. To bootstrap the training process, we develop a rule-based method for mining noisy pseudo-l…
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
Jun Wang, Shiyi Lan, Mingfei Gao +1
Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. How…
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
Luyu Yang, Yan Wang, Mingfei Gao +4
Semi-supervised domain adaptation (SSDA) aims to adapt models trained from a labeled source domain to a different but related target domain, from which unlabeled data and a small s…
WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
Mingfei Gao, Yingbo Zhou, Ran Xu +2
Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious ann…
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost
Mingfei Gao, Zizhao Zhang, Guo Yu +3
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 performanc…
WSLLN: Weakly Supervised Natural Language Localization Networks
Mingfei Gao, Larry S. Davis, Richard Socher +1
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries. To learn the correspondence between visual se…
Goal-oriented Object Importance Estimation in On-road Driving Videos
Mingfei Gao, Ashish Tawari, Sujitha Martin
We formulate a new problem as Object Importance Estimation (OIE) in on-road driving videos, where the road users are considered as important objects if they have influence on the c…
StartNet: Online Detection of Action Start in Untrimmed Videos
Mingfei Gao, Mingze Xu, Larry S. Davis +2
We propose StartNet to address Online Detection of Action Start (ODAS) where action starts and their associated categories are detected in untrimmed, streaming videos. Previous met…
Temporal Recurrent Networks for Online Action Detection
Mingze Xu, Mingfei Gao, Yi-Ting Chen +2
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. How…
NISP: Pruning Networks using Neuron Importance Score Propagation
Ruichi Yu, Ang Li, Chun-Fu Chen +6
To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering statistics of an individual layer or two…
C-WSL: Count-guided Weakly Supervised Localization
Mingfei Gao, Ang Li, Ruichi Yu +2
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 localizatio…
Dynamic Zoom-in Network for Fast Object Detection in Large Images
Mingfei Gao, Ruichi Yu, Ang Li +2
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 resol…