no code implementations • Findings (EMNLP) 2021 • Lulu Zhao, Weihao Zeng, Weiran Xu, Jun Guo
Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process.
no code implementations • ICON 2021 • Jingxuan Yang, Si Li, Jun Guo
In this paper, we formulate the target-guided conversation as a problem of multi-turn topic prediction and model it under the framework of Markov decision process (MDP).
no code implementations • 22 Mar 2024 • Jun Guo, Xiaojian Ma, Yue Fan, Huaping Liu, Qing Li
Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, withwide-ranging applications in embodied agents and augmented reality systems.
1 code implementation • 2 Mar 2024 • Zijin Yin, Kongming Liang, Bing Li, Zhanyu Ma, Jun Guo
We evaluate a broad variety of semantic segmentation models, spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations.
no code implementations • 12 Dec 2023 • Kongming Liang, Xinran Wang, Rui Wang, Donghui Gao, Ling Jin, Weidong Liu, Xiatian Zhu, Zhanyu Ma, Jun Guo
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization.
1 code implementation • 26 Nov 2023 • Junhui Yin, Wei Yin, Hao Chen, Xuqian Ren, Zhanyu Ma, Jun Guo, Yifan Liu
These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays.
no code implementations • 15 Oct 2023 • Simin Li, Ruixiao Xu, Jun Guo, Pu Feng, Jiakai Wang, Aishan Liu, Yaodong Yang, Xianglong Liu, Weifeng Lv
Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios.
no code implementations • 3 Aug 2023 • Xingkun Niu, Feng Gao, Shaojie Hou, Shihao Liu, Xinmin Zhao, Jun Guo, Liping Wang, Feng Zhang
Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling.
1 code implementation • 2 Aug 2023 • Jun Guo, Aishan Liu, Xingyu Zheng, Siyuan Liang, Yisong Xiao, Yichao Wu, Xianglong Liu
However, these defenses are now suffering problems of high inference computational overheads and unfavorable trade-offs between benign accuracy and stealing robustness, which challenges the feasibility of deployed models in practice.
no code implementations • 3 Jul 2023 • Ruoyang Zhao, Feng Gao, Maoyu Li, Xingkun Niu, Shihao Liu, Xinmin Zhao, Liping Wang, Jun Guo, Feng Zhang
Hydrophobic domains provide specific microenvironment for essential functional activities in life.
1 code implementation • 23 May 2023 • Mingkun Li, Peng Xu, Chun-Guang Li, Jun Guo
In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change.
Ranked #1 on Unsupervised Person Re-Identification on PRCC
Clothes Changing Person Re-Identification Contrastive Learning +3
1 code implementation • 19 Feb 2023 • Aishan Liu, Jun Guo, Jiakai Wang, Siyuan Liang, Renshuai Tao, Wenbo Zhou, Cong Liu, Xianglong Liu, DaCheng Tao
In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-critical scenario.
1 code implementation • 7 Feb 2023 • Simin Li, Jun Guo, Jingqiao Xiu, Pu Feng, Xin Yu, Aishan Liu, Wenjun Wu, Xianglong Liu
To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
1 code implementation • 30 Nov 2022 • Jijie Wu, Dongliang Chang, Aneeshan Sain, Xiaoxu Li, Zhanyu Ma, Jie Cao, Jun Guo, Yi-Zhe Song
Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).
1 code implementation • 1 Jun 2022 • Tian Zhang, Kongming Liang, Ruoyi Du, Xian Sun, Zhanyu Ma, Jun Guo
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set.
no code implementations • 27 May 2022 • Lu Yang, He Jiang, Qing Song, Jun Guo
Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality.
no code implementations • 17 Apr 2022 • Jun Guo, Yonghong Chen, Yihang Hao, Zixin Yin, Yin Yu, Simin Li
To overcome the challenge, we propose MARLSafe, the first robustness testing framework for c-MARL algorithms.
no code implementations • 7 Feb 2022 • Mingkun Li, Shupeng Cheng, Peng Xu, Xiatian Zhu, Chun-Guang Li, Jun Guo
We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment.
Clustering Unsupervised Long Term Person Re-Identification +2
1 code implementation • 6 Dec 2021 • Dongliang Chang, Kaiyue Pang, Ruoyi Du, Zhanyu Ma, Yi-Zhe Song, Jun Guo
1 lays out our approach in answering this question.
1 code implementation • 6 Dec 2021 • Ruoyi Du, Dongliang Chang, Zhanyu Ma, Yi-Zhe Song, Jun Guo
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for.
no code implementations • 25 Oct 2021 • Lulu Zhao, Fujia Zheng, Keqing He, Weihao Zeng, Yuejie Lei, Huixing Jiang, Wei Wu, Weiran Xu, Jun Guo, Fanyu Meng
Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet.
1 code implementation • 15 Jun 2021 • Mingkun Li, Chun-Guang Li, Jun Guo
To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively.
1 code implementation • ACL 2021 • Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Nianwen Xue, Ji-Rong Wen
A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.
Ranked #5 on Discourse Parsing on STAC
no code implementations • 1 Apr 2021 • Junhui Yin, Zhanyu Ma, Jiyang Xie, Shibo Nie, Kongming Liang, Jun Guo
Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching.
Cross-Modality Person Re-identification Person Re-Identification
no code implementations • 1 Apr 2021 • Junhui Yin, Jiayan Qiu, Siqing Zhang, Jiyang Xie, Zhanyu Ma, Jun Guo
Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models.
1 code implementation • 11 Mar 2021 • Zijin Yin, Kongming Liang, Zhanyu Ma, Jun Guo
However, previous methods only focus on learning the dependencies between the position within an individual image and ignore the contextual relation across different images.
no code implementations • 24 Jan 2021 • Jun Guo, Wei Bao, Jiakai Wang, Yuqing Ma, Xinghai Gao, Gang Xiao, Aishan Liu, Jian Dong, Xianglong Liu, Wenjun Wu
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
1 code implementation • 21 Jan 2021 • Tian Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category.
Ranked #32 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 1 Jan 2021 • Lulu Zhao, Zeyuan Yang, Weiran Xu, Sheng Gao, Jun Guo
In this paper, we present a Knowledge Graph Enhanced Dual-Copy network (KGEDC), a novel framework for abstractive dialogue summarization with conversational structure and factual knowledge.
no code implementations • ICCV 2021 • Mingfei Cheng, Kaili Zhao, Xuhong Guo, Yajing Xu, Jun Guo
To the best of our knowledge, this is the first work that jointly addresses topology preserving and feature refinement for CSS.
1 code implementation • 21 Dec 2020 • Siqing Zhang, Ruoyi Du, Dongliang Chang, Zhanyu Ma, Jun Guo
Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions.
Ranked #37 on Fine-Grained Image Classification on CUB-200-2011
no code implementations • COLING 2020 • Lulu Zhao, Weiran Xu, Jun Guo
A masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue.
1 code implementation • CVPR 2021 • Dongliang Chang, Kaiyue Pang, Yixiao Zheng, Zhanyu Ma, Yi-Zhe Song, Jun Guo
For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo".
Ranked #16 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 17 Nov 2020 • Jiyang Xie, Zhanyu Ma, Jing-Hao Xue, Guoqiang Zhang, Jun Guo
In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.
1 code implementation • 11 Oct 2020 • Jiyang Xie, Zhanyu Ma, and Jianjun Lei, Guoqiang Zhang, Jing-Hao Xue, Zheng-Hua Tan, Jun Guo
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Ji-Rong Wen, Nianwen Xue
Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
no code implementations • 13 Sep 2020 • Junhui Yin, Jiayan Qiu, Siqing Zhang, Zhanyu Ma, Jun Guo
To this end, we propose a Self-Supervised Knowledge Distillation (SSKD) technique containing two modules, the identity learning and the soft label learning.
1 code implementation • 27 Jun 2020 • Xiaoxu Li, Liyun Yu, Xiaochen Yang, Zhanyu Ma, Jing-Hao Xue, Jie Cao, Jun Guo
Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small.
2 code implementations • 23 May 2020 • Junxu Cao, Qi Chen, Jun Guo, Ruichao Shi
For object detection, how to address the contradictory requirement between feature map resolution and receptive field on high-resolution inputs still remains an open question.
Ranked #72 on Object Detection on COCO test-dev
1 code implementation • 20 Apr 2020 • Xiaoxu Li, Dongliang Chang, Zhanyu Ma, Zheng-Hua Tan, Jing-Hao Xue, Jie Cao, Jingyi Yu, Jun Guo
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
1 code implementation • 10 Mar 2020 • Jiyang Xie, Dongliang Chang, Zhanyu Ma, Guo-Qiang Zhang, Jun Guo
In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.
1 code implementation • 9 Mar 2020 • Junhui Yin, Siqing Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo
This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD).
no code implementations • 8 Mar 2020 • Fangyi Zhu, Jenq-Neng Hwang, Zhanyu Ma, Guang Chen, Jun Guo
Thereafter, we construct a new dataset, providing consistent object-sentence pairs, to facilitate effective cross-modal learning.
5 code implementations • ECCV 2020 • Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia, Jiyang Xie, Zhanyu Ma, Yi-Zhe Song, Jun Guo
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #17 on Fine-Grained Image Classification on Stanford Cars
2 code implementations • 8 Mar 2020 • Dongliang Chang, Aneeshan Sain, Zhanyu Ma, Yi-Zhe Song, Jun Guo
The key insight lies with how we exploit the mutually beneficial information between two networks; (a) to separate samples of known and unknown classes, (b) to maximize the domain confusion between source and target domain without the influence of unknown samples.
no code implementations • 28 Feb 2020 • Jinlong Kang, Jiaxiang Zheng, Heng Bai, Xiaoting Xue, Yang Zhou, Jun Guo
To solve this problem, in this paper, we describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning.
no code implementations • 21 Feb 2020 • Peng Xu, Kun Liu, Tao Xiang, Timothy M. Hospedales, Zhanyu Ma, Jun Guo, Yi-Zhe Song
Existing sketch-analysis work studies sketches depicting static objects or scenes.
3 code implementations • 11 Feb 2020 • Dongliang Chang, Yifeng Ding, Jiyang Xie, Ayan Kumar Bhunia, Xiaoxu Li, Zhanyu Ma, Ming Wu, Jun Guo, Yi-Zhe Song
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #29 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • 31 Dec 2019 • Lanfei Wang, Lingxi Xie, Tianyi Zhang, Jun Guo, Qi Tian
Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision.
no code implementations • IEEE Access 2019 • Junjian Zhang, Chun-Guang Li, Tianming Du, Honggang Zhang, Jun Guo
Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points.
no code implementations • 10 Sep 2019 • Yitong Meng, Xinyan Dai, Xiao Yan, James Cheng, Weiwen Liu, Benben Liao, Jun Guo, Guangyong Chen
Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users.
no code implementations • 10 Sep 2019 • Yitong Meng, Guangyong Chen, Benben Liao, Jun Guo, Weiwen Liu
We further adopt the idea of CF and propose Wasserstein CF (WCF) to improve the recommendation performance on cold-start items.
no code implementations • 3 Sep 2019 • Yuanyuan Qi, Jiayue Zhang, Weiran Xu, Jun Guo
In this paper, we propose a salient-context based semantic matching method to improve relevance ranking in information retrieval.
1 code implementation • NAACL 2019 • Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, Nianwen Xue
Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context.
no code implementations • CVPR 2019 • Junjian Zhang, Chun-Guang Li, Chong You, Xianbiao Qi, Honggang Zhang, Jun Guo, Zhouchen Lin
However, the applicability of subspace clustering has been limited because practical visual data in raw form do not necessarily lie in such linear subspaces.
Ranked #2 on Image Clustering on Extended Yale-B
no code implementations • 14 Apr 2019 • Jingxuan Yang, Jun Xu, Jianzhuo Tong, Sheng Gao, Jun Guo, Ji-Rong Wen
In the offline phase, IERT pre-trains deep item representations conditioning on their transaction contexts.
no code implementations • 13 Feb 2019 • Zhanyu Ma, Jalil Taghia, Jun Guo
Recently, an improved framework, namely the extended variational inference (EVI), has been introduced and applied to derive analytically tractable solution by employing lower-bound approximation to the variational objective function.
no code implementations • 20 Jan 2019 • Saeed Karimi-Bidhendi, Jun Guo, Hamid Jafarkhani
We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs).
Information Theory Information Theory
no code implementations • 2 Aug 2018 • Jiyang Xie, Zhanyu Ma, Jun Guo
Using artificial neural network for the prediction of heat demand has attracted more and more attention.
no code implementations • 28 Jul 2018 • Jiyang Xie, Jiaxin Guo, Zhanyu Ma, Jing-Hao Xue, Qie Sun, Hailong Li, Jun Guo
ENN and ARIMA are used to predict seasonal and trend components, respectively.
no code implementations • 8 Jul 2018 • Jiyang Xie, Zhanyu Ma, Guo-Qiang Zhang, Jing-Hao Xue, Jen-Tzung Chien, Zhiqing Lin, Jun Guo
In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.
no code implementations • 21 May 2018 • Chun-Guang Li, Junjian Zhang, Jun Guo
Subspace clustering refers to the problem of segmenting high dimensional data drawn from a union of subspaces into the respective subspaces.
no code implementations • 20 May 2018 • Ruo-Pei Guo, Chun-Guang Li, Yonghua Li, Jia-Ru Lin, Jun Guo
In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking.
1 code implementation • CVPR 2018 • Peng Xu, Yongye Huang, Tongtong Yuan, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Zhanyu Ma, Jun Guo
Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.
no code implementations • 27 Feb 2018 • Feiyun Zhu, Jun Guo, Ruoyu Li, Junzhou Huang
Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.
1 code implementation • 14 Aug 2017 • Feiyun Zhu, Jun Guo, Zheng Xu, Peng Liao, Junzhou Huang
Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health.
no code implementations • 30 May 2017 • Zhanyu Ma, Jing-Hao Xue, Arne Leijon, Zheng-Hua Tan, Zhen Yang, Jun Guo
In this paper, we propose novel strategies for neutral vector variable decorrelation.
no code implementations • 28 May 2017 • Peng Xu, Qiyue Yin, Yongye Huang, Yi-Zhe Song, Zhanyu Ma, Liang Wang, Tao Xiang, W. Bastiaan Kleijn, Jun Guo
Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo.
Ranked #5 on Sketch-Based Image Retrieval on Chairs
no code implementations • 13 Feb 2017 • Hong Yu, Zheng-Hua Tan, Zhanyu Ma, Jun Guo
In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech.
no code implementations • CVPR 2017 • Jun Guo, Hongyang Chao
We consider the compression artifacts reduction problem, where a compressed image is transformed into an artifact-free image.
no code implementations • ICCV 2015 • Chun-Guang Li, Zhouchen Lin, Honggang Zhang, Jun Guo
State of the art approaches for Semi-Supervised Learning (SSL) usually follow a two-stage framework -- constructing an affinity matrix from the data and then propagating the partial labels on this affinity matrix to infer those unknown labels.
no code implementations • CVPR 2015 • Yonggang Qi, Yi-Zhe Song, Tao Xiang, Honggang Zhang, Timothy Hospedales, Yi Li, Jun Guo
We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks.
no code implementations • 16 Feb 2015 • Xianbiao Qi, Guoying Zhao, Chun-Guang Li, Jun Guo, Matti Pietikäinen
Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence for diagnosis of autoimmune diseases.
no code implementations • CVPR 2014 • Weihong Deng, Jiani Hu, Jun Guo
We extend the classical linear discriminant analysis (LDA) technique to linear ranking analysis (LRA), by considering the ranking order of classes centroids on the projected subspace.
no code implementations • CVPR 2013 • Weihong Deng, Jiani Hu, Jun Guo
The success of sparse representation based classification (SRC) has largely boosted the research of sparsity based face recognition in recent years.