443 citations · 544 across the 17 of their papers we have counts for
17 papers
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
Yong Liu, Hang Dong, Boyang Liang +6
Recent years have witnessed a few attempts of vision transformers for single image super-resolution (SISR). Since the high resolution of intermediate features in SISR models increa…
End-to-End Beam Retrieval for Multi-Hop Question Answering
Jiahao Zhang, Haiyang Zhang, Dongmei Zhang +2
Multi-hop question answering (QA) involves finding multiple relevant passages and step-by-step reasoning to answer complex questions, indicating a retrieve-and-read paradigm. Howev…
RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer
Jiahao Wang, Songyang Zhang, Yong Liu +6
This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs),…
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Qianwen Meng, Hangwei Qian, Yong Liu +3
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recentl…
Inductive Graph Transformer for Delivery Time Estimation
Xin Zhou, Jinglong Wang, Yong Liu +3
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase exp…
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Haixu Wu, Tengge Hu, Yong Liu +3
Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal vari…
Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
Yongwei Wang, Yong Liu, Zhiqi Shen
Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to ev…
Learning Quality-aware Dynamic Memory for Video Object Segmentation
Yong Liu, Ran Yu, Fei Yin +4
Recently, several spatial-temporal memory-based methods have verified that storing intermediate frames and their masks as memory are helpful to segment target objects in videos. Ho…
UniInst: Unique Representation for End-to-End Instance Segmentation
Yimin Ou, Rui Yang, Lufan Ma +5
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e.g., multiple boxes, grids, and anch…
A Survey of Visual Sensory Anomaly Detection
Xi Jiang, Guoyang Xie, Jinbao Wang +4
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semanti…
Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application
Yulan Hu, Yong Liu
Benefiting from the strong ability of the pre-trained model, the research on Chinese Word Segmentation (CWS) has made great progress in recent years. However, due to massive comput…
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Kaichao You, Yong Liu, Ziyang Zhang +3
Model hubs with many pre-trained models (PTMs) have become a cornerstone of deep learning. Although built at a high cost, they remain \emph{under-exploited} -- practitioners usuall…
Go Wider Instead of Deeper
Fuzhao Xue, Ziji Shi, Futao Wei +3
More transformer blocks with residual connections have recently achieved impressive results on various tasks. To achieve better performance with fewer trainable parameters, recent…
Concurrent Adversarial Learning for Large-Batch Training
Yong Liu, Xiangning Chen, Minhao Cheng +2
Large-batch training has become a commonly used technique when training neural networks with a large number of GPU/TPU processors. As batch size increases, stochastic optimizers te…
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
Kaichao You, Yong Liu, Jianmin Wang +1
This paper studies task adaptive pre-trained model selection, an underexplored problem of assessing pre-trained models for the target task and select best ones from the model zoo \…
Automatic Data Augmentation by Learning the Deterministic Policy
Yinghuan Shi, Tiexin Qin, Yong Liu +3
Aiming to produce sufficient and diverse training samples, data augmentation has been demonstrated for its effectiveness in training deep models. Regarding that the criterion of th…
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
Weixun Wang, Tianpei Yang, Yong Liu +6
In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's se…