676 citations · 4.7k across the 170 of their papers we have counts for
32 papers · 1 filter
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Shuai Lin, Pan Zhou, Xiaodan Liang +4
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded di…
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding
Qingxing Cao, Bailin Li, Xiaodan Liang +2
Though beneficial for encouraging the Visual Question Answering (VQA) models to discover the underlying knowledge by exploiting the input-output correlation beyond image and text c…
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
Tao Pu, Tianshui Chen, Yuan Xie +2
Recognizing human emotion/expressions automatically is quite an expected ability for intelligent robotics, as it can promote better communication and cooperation with humans. Curre…
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement
Yinya Huang, Meng Fang, Xunlin Zhan +3
When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commo…
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
Yubei Xiao, Ke Gong, Pan Zhou +3
Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulat…
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
Lingbo Liu, Jiaqi Chen, Hefeng Wu +3
Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited…
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
Junfan Lin, Zhongzhan Huang, Keze Wang +3
Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the po…
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
Yangxin Wu, Gengwei Zhang, Hang Xu +2
Panoptic segmentation is posed as a new popular test-bed for the state-of-the-art holistic scene understanding methods with the requirement of simultaneously segmenting both foregr…
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning
Hongjun Wang, Guanbin Li, Xiaobai Liu +1
Although deep convolutional neural networks (CNNs) have demonstrated remarkable performance on multiple computer vision tasks, researches on adversarial learning have shown that de…
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems
Jinghui Qin, Lihui Lin, Xiaodan Liang +2
A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs. Herei…
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems
Lishan Huang, Zheng Ye, Jinghui Qin +2
Automatically evaluating dialogue coherence is a challenging but high-demand ability for developing high-quality open-domain dialogue systems. However, current evaluation metrics c…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Recognizing multiple labels of an image is a practical yet challenging task, and remarkable progress has been achieved by searching for semantic regions and exploiting label depend…
Reinforcement Learning for Weakly Supervised Temporal Grounding of Natural Language in Untrimmed Videos
Jie Wu, Guanbin Li, Xiaoguang Han +1
Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval. We focus on the weakl…
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection
Ganlong Zhao, Guanbin Li, Ruijia Xu +1
Object detectors are usually trained with large amount of labeled data, which is expensive and labor-intensive. Pre-trained detectors applied to unlabeled dataset always suffer fro…
Online Alternate Generator against Adversarial Attacks
Haofeng Li, Yirui Zeng, Guanbin Li +2
The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks. However, deep learning models…
Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
Yang Liu, Keze Wang, Guanbin Li +1
Existing vision-based action recognition is susceptible to occlusion and appearance variations, while wearable sensors can alleviate these challenges by capturing human motion with…
Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View
Junpeng Tan, Yukai Shi, Zhijing Yang +2
Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithm…
Component Divide-and-Conquer for Real-World Image Super-Resolution
Pengxu Wei, Ziwei Xie, Hannan Lu +4
In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring t…
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning
Bailin Li, Bowen Wu, Jiang Su +2
Finding out the computational redundant part of a trained Deep Neural Network (DNN) is the key question that pruning algorithms target on. Many algorithms try to predict model perf…
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
Yuan Xie, Tianshui Chen, Tao Pu +2
Data inconsistency and bias are inevitable among different facial expression recognition (FER) datasets due to subjective annotating process and different collecting conditions. Re…
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
Tianshui Chen, Tao Pu, Hefeng Wu +3
To address the problem of data inconsistencies among different facial expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs) have been extensively devised i…
Fine-Grained Image Captioning with Global-Local Discriminative Objective
Jie Wu, Tianshui Chen, Hefeng Wu +3
Significant progress has been made in recent years in image captioning, an active topic in the fields of vision and language. However, existing methods tend to yield overly general…
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
Kai Zhang, Shuhang Gu, Radu Timofte +60
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input im…