activity
20202023
most citedStructured Multimodal Attentions for TextVQA

16 citations · 51 across the 13 of their papers we have counts for

collaborators

13 papers

cs.AI2023★ 3 cited

Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks

Payal Mohapatra, Akash Pandey, Yueyuan Sui +1

Human emotion understanding is pivotal in making conversational technology mainstream. We view speech emotion understanding as a perception task which is a more realistic setting.…

cs.LG2023★ 1 cited

Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling

Ruochen Jiao, Yixuan Wang, Xiangguo Liu +2

Trajectory generation and trajectory prediction are two critical tasks in autonomous driving, which generate various trajectories for testing during development and predict the tra…

cs.CV2023★ 3 cited

Collaborative Multi-Agent Video Fast-Forwarding

Shuyue Lan, Zhilu Wang, Ermin Wei +2

Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as a team of robots with cameras, could work collabor…

cs.CV2023★ 5 cited

MDoc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis

Hiuyi Cheng, Peirong Zhang, Sihang Wu +6

Document layout analysis is a crucial prerequisite for document understanding, including document retrieval and conversion. Most public datasets currently contain only PDF document…

cs.MA2023★ 4 cited

Attrition-Aware Adaptation for Multi-Agent Patrolling

Anthony Goeckner, Xinliang Li, Ermin Wei +1

Multi-agent patrolling is a key problem in a variety of domains such as intrusion detection, area surveillance, and policing which involves repeated visits by a group of agents to…

cs.CV2023★ 2 cited

Leapfrog Diffusion Model for Stochastic Trajectory Prediction

Weibo Mao, Chenxin Xu, Qi Zhu +2

To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models…

cs.LG2022★ 3 cited

Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints

Hengquan Guo, Qi Zhu, Xin Liu

This paper studies the problem of stochastic continuum-armed bandit with constraints (SCBwC), where we optimize a black-box reward function subject to a black-box constraint…

cs.CV2022

Adversarial and Random Transformations for Robust Domain Adaptation and Generalization

Liang Xiao, Jiaolong Xu, Dawei Zhao +3

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentati…

cs.CV2022★ 5 cited

Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminator

Yuxin Kong, Canjie Luo, Weihong Ma +4

Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the st…

cs.LG2022★ 6 cited

Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

Ruochen Jiao, Xiangguo Liu, Takami Sato +2

Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and many other autonomous systems. Recent works demonstrate that adversarial attacks…

cs.CV2021★ 1 cited

Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads

Chenyu Gao, Qi Zhu, Peng Wang +1

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the V…

cs.CV2020★ 2 cited

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps

Qi Zhu, Chenyu Gao, Peng Wang +1

Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices.…

cs.CV2020★ 16 cited

Structured Multimodal Attentions for TextVQA

Chenyu Gao, Qi Zhu, Peng Wang +4

In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above. SMA first uses a structural graph represent…