674 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Illinois ChicagoUS13 papers
- Singapore Management UniversitySG11 papers
- Carnegie Mellon UniversityUS8 papers
- Georgia Institute of TechnologyUS7 papers
- Princeton UniversityUS7 papers
- Nanyang Technological UniversitySG6 papers
- University of Illinois Urbana-ChampaignUS6 papers
- Agency for Science, Technology and ResearchSG5 papers
- Chinese University of Hong KongHK5 papers
- University of Maryland, College ParkUS5 papers
- University of WashingtonUS5 papers
- Beihang UniversityCN4 papers
21 papers · 1 filter
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…
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks
Hung Le, Nancy F. Chen, Steven C. H. Hoi
Neural module networks (NMN) have achieved success in image-grounded tasks such as Visual Question Answering (VQA) on synthetic images. However, very limited work on NMN has been s…
BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues
Hung Le, Doyen Sahoo, Nancy F. Chen +1
Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances whi…
Shape-Texture Debiased Neural Network Training
Yingwei Li, Qihang Yu, Mingxing Tan +5
Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, dep…
Adaptive Task Sampling for Meta-Learning
Chenghao Liu, Zhihao Wang, Doyen Sahoo +3
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classif…
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…
The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation
Tao Wang, Yu Li, Bingyi Kang +5
Most existing object instance detection and segmentation models only work well on fairly balanced benchmarks where per-category training sample numbers are comparable, such as COCO…
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…
Prototypical Contrastive Learning of Unsupervised Representations
Junnan Li, Pan Zhou, Caiming Xiong +1
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive…
VD-BERT: A Unified Vision and Dialog Transformer with BERT
Yue Wang, Shafiq Joty, Michael R. Lyu +3
Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior wor…
Towards Noise-resistant Object Detection with Noisy Annotations
Junnan Li, Caiming Xiong, Richard Socher +1
Training deep object detectors requires significant amount of human-annotated images with accurate object labels and bounding box coordinates, which are extremely expensive to acqu…
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…
Learning from Noisy Anchors for One-stage Object Detection
Hengduo Li, Zuxuan Wu, Chen Zhu +3
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their inte…
Classification Calibration for Long-tail Instance Segmentation
Tao Wang, Yu Li, Bingyi Kang +5
Remarkable progress has been made in object instance detection and segmentation in recent years. However, existing state-of-the-art methods are mostly evaluated with fairly balance…
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…
Recent Advances in Deep Learning for Object Detection
Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi
Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of cer…
Distilled Siamese Networks for Visual Tracking
Jianbing Shen, Yuanpei Liu, Xingping Dong +3
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from…
Deep Learning for Image Super-resolution: A Survey
Zhihao Wang, Jian Chen, Steven C. H. Hoi
Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed rema…
AdaFrame: Adaptive Frame Selection for Fast Video Recognition
Zuxuan Wu, Caiming Xiong, Chih-Yao Ma +2
We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame contains a Long Short-Term Memory network augment…
End-to-End Dense Video Captioning with Masked Transformer
Luowei Zhou, Yingbo Zhou, Jason J. Corso +2
Dense video captioning aims to generate text descriptions for all events in an untrimmed video. This involves both detecting and describing events. Therefore, all previous methods…
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
Jiasen Lu, Caiming Xiong, Devi Parikh +1
Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However,…