Search Results for author: Qing Liu

Found 47 papers, 9 papers with code

Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment

no code implementations ECCV 2020 Qing Liu, Orchid Majumder, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto

This process enables incrementally improving the model by processing multiple learning episodes, each representing a different learning task, even with few training examples.

Few-Shot Learning Incremental Learning

TOT: Topology-Aware Optimal Transport For Multimodal Hate Detection

no code implementations27 Feb 2023 Linhao Zhang, Li Jin, Xian Sun, Guangluan Xu, Zequn Zhang, Xiaoyu Li, Nayu Liu, Shiyao Yan, Qing Liu

Multimodal hate detection, which aims to identify harmful content online such as memes, is crucial for building a wholesome internet environment.

HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace

no code implementations21 Feb 2023 Yunzhong He, Cong Zhang, Ruoyan Kong, Chaitanya Kulkarni, Qing Liu, Ashish Gandhe, Amit Nithianandan, Arul Prakash

Query categorization at customer-to-customer e-commerce platforms like Facebook Marketplace is challenging due to the vagueness of search intent, noise in real-world data, and imbalanced training data across languages.

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

no code implementations22 Dec 2022 Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O'Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, Ves Stoyanov

To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.

Language Modelling Meta-Learning +2

SceneComposer: Any-Level Semantic Image Synthesis

no code implementations21 Nov 2022 Yu Zeng, Zhe Lin, Jianming Zhang, Qing Liu, John Collomosse, Jason Kuen, Vishal M. Patel

We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes.

Image Generation

A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing

no code implementations19 Nov 2022 Yi Yang, Zhong-Qiu Zhao, Quan Bai, Qing Liu, Weihua Li

Due to the dynamic nature, the proposed algorithms can also estimate true labels online without re-visiting historical data.

Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022)

no code implementations7 Nov 2022 Qing Liu, Wenli Yang, Shiqing Wu

Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI).

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

1 code implementation11 Jul 2022 Yixiong Liang, Shuo Feng, Qing Liu, Hulin Kuang, Jianfeng Liu, Liyan Liao, Yun Du, Jianxin Wang

To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.

Recent Advances for Quantum Neural Networks in Generative Learning

no code implementations7 Jun 2022 Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, DaCheng Tao

Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.

BIG-bench Machine Learning Quantum Machine Learning

Unified Structure Generation for Universal Information Extraction

1 code implementation ACL 2022 Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu

Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.

Few-shot Named Entity Recognition with Self-describing Networks

1 code implementation ACL 2022 Jiawei Chen, Qing Liu, Hongyu Lin, Xianpei Han, Le Sun

In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.

Few-shot NER Named Entity Recognition

Uncovering system vulnerability and criticality of human brain under evolving neuropathological events in Alzheimer's Disease

no code implementations22 Jan 2022 Jingwen Zhang, Qing Liu, Haorui Zhang, Michelle Dai, Qianqian Song, Defu Yang, Guorong Wu, Minghan Chen

Despite the striking efforts in investigating neurobiological factors behind the acquisition of beta-amyloid (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spread throughout the brain remain elusive.

Deep Open Set Identification for RF Devices

no code implementations5 Dec 2021 Qing Wang, Qing Liu, Zihao Zhang, HaoYu Fang, Xi Zheng

Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process.

M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images

1 code implementation30 Oct 2021 Qing Liu, Haotian Liu, Wei Ke, Yixiong Liang

It reassembles features in a dimension-reduced feature space and simultaneously aggregates multiple features inside a large predefined region into multiple target features.

Lesion Segmentation

Context-aware Reranking with Utility Maximization for Recommendation

no code implementations18 Oct 2021 Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, Yong Yu

As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.

Graph Attention Recommendation Systems

Fine-grained Entity Typing via Label Reasoning

no code implementations EMNLP 2021 Qing Liu, Hongyu Lin, Xinyan Xiao, Xianpei Han, Le Sun, Hua Wu

Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.

Entity Typing

Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement

no code implementations23 Aug 2021 Qing Liu, Longbing Cao

Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes.

Bayesian Inference

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

no code implementations9 Jun 2021 Xiangli Yang, Qing Liu, Rong Su, Ruiming Tang, Zhirong Liu, Xiuqiang He

The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.

Click-Through Rate Prediction Recommendation Systems +1

Visual analogy: Deep learning versus compositional models

no code implementations14 May 2021 Nicholas Ichien, Qing Liu, Shuhao Fu, Keith J. Holyoak, Alan Yuille, Hongjing Lu

We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations.

Visual Analogies

COVID-19 Modeling: A Review

no code implementations16 Apr 2021 Longbing Cao, Qing Liu

The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling.


Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

no code implementations CVPR 2021 Qing Liu, Vignesh Ramanathan, Dhruv Mahajan, Alan Yuille, Zhenheng Yang

However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions.

Instance Segmentation Semantic Segmentation +1

Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer's Disease using Longitudinal Neuroimaging Data

no code implementations7 Mar 2021 Qing Liu, Defu Yang, Jingwen Zhang, Ziming Wei, Guorong Wu, Minghan Chen

Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease.


Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization

no code implementations25 Jan 2021 Cheng Xie, Hongxin Xiang, Ting Zeng, Yun Yang, Beibei Yu, Qing Liu

CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network.

Image Classification Retrieval +1

A Deep Retinal Image Quality Assessment Network with Salient Structure Priors

no code implementations31 Dec 2020 Ziwen Xu, Beiji Zou, Qing Liu

Dual-branch SalStructIQA contains two CNN branches and one is guided by large-size salient structures while the other is guided by tiny-size salient structures.

Image Quality Assessment

Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation from Colour Fundus Images

no code implementations3 Dec 2020 Qing Liu, Haotian Liu, Yixiong Liang

In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates.

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

no code implementations1 Nov 2020 Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu

To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.

Click-Through Rate Prediction Learning-To-Rank +1

Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion

no code implementations28 Jun 2020 Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun, Alan Yuille

The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.

Image Classification object-detection +2

Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion

1 code implementation CVPR 2020 Adam Kortylewski, Ju He, Qing Liu, Alan Yuille

Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion.

General Classification

Incremental Meta-Learning via Indirect Discriminant Alignment

no code implementations11 Feb 2020 Qing Liu, Orchid Majumder, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto

Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal representation for a specific few-shot task involving classes which were not seen during the meta-training phase.

Incremental Learning Meta-Learning

Localizing Occluders with Compositional Convolutional Networks

no code implementations18 Nov 2019 Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille

Our experimental results demonstrate that the proposed extensions increase the model's performance at localizing occluders as well as at classifying partially occluded objects.

A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation

no code implementations5 Nov 2019 Qing Liu, Beiji Zou, Yang Zhao, Yixiong Liang

To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.

Dual-attention Focused Module for Weakly Supervised Object Localization

no code implementations11 Sep 2019 Yukun Zhou, Zailiang Chen, Hailan Shen, Qing Liu, Rongchang Zhao, Yixiong Liang

In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them.

Object Recognition Weakly Supervised Object Localization +1

Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion

no code implementations28 May 2019 Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille

In this work, we combine DCNNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks.

General Classification Image Classification

DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation

no code implementations18 Apr 2019 Qing Liu, Xiaopeng Hong, Wei Ke, Zailiang Chen, Beiji Zou

In this paper, we propose a novel segmentation approach, named Cartesian-polar dual-domain network (DDNet), which for the first time considers the complementary of the Cartesian domain and the polar domain.

Feature Importance

Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval

1 code implementation ICCV 2019 Qing Liu, Lingxi Xie, Huiyu Wang, Alan Yuille

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications.

Domain Adaptation Retrieval +2

Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

no code implementations27 Dec 2018 Chandra Khatri, Behnam Hedayatnia, Anu Venkatesh, Jeff Nunn, Yi Pan, Qing Liu, Han Song, Anna Gottardi, Sanjeev Kwatra, Sanju Pancholi, Ming Cheng, Qinglang Chen, Lauren Stubel, Karthik Gopalakrishnan, Kate Bland, Raefer Gabriel, Arindam Mandal, Dilek Hakkani-Tur, Gene Hwang, Nate Michel, Eric King, Rohit Prasad

In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.

Knowledge Graphs Management +4

Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data

1 code implementation ICCV 2019 Yutong Bai, Qing Liu, Lingxi Xie, Weichao Qiu, Yan Zheng, Alan Yuille

In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques).

Semantic Part Detection

Unleashing the Potential of CNNs for Interpretable Few-Shot Learning

no code implementations ICLR 2018 Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille

Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs.

Few-Shot Learning

Few-shot Learning by Exploiting Visual Concepts within CNNs

no code implementations22 Nov 2017 Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille

In this work, we address these limitations of CNNs by developing novel, flexible, and interpretable models for few-shot learning.

Few-Shot Learning

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