1 code implementation • 6 Mar 2023 • An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua
Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.
1 code implementation • 6 Mar 2023 • Xiang Wang, Shiwei Zhang, Jun Cen, Changxin Gao, Yingya Zhang, Deli Zhao, Nong Sang
Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task.
1 code implementation • 10 Feb 2023 • An Zhang, Jingnan Zheng, Xiang Wang, Yancheng Yuan, Tat-Seng Chua
Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios.
1 code implementation • 9 Jan 2023 • Xiang Wang, Shiwei Zhang, Zhiwu Qing, Zhengrong Zuo, Changxin Gao, Rong Jin, Nong Sang
To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric.
1 code implementation • 20 Dec 2022 • Yinwei Wei, Xiang Wang, Liqiang Nie, Shaoyu Li, Dingxian Wang, Tat-Seng Chua
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model.
no code implementations • 25 Nov 2022 • Tianpeng Bao, Jiadong Chen, Wei Li, Xiang Wang, Jingjing Fei, Liwei Wu, Rui Zhao, Ye Zheng
However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.
no code implementations • 19 Nov 2022 • Xiang Wang, Yimin Yang, Zhichang Guo, Zhili Zhou, Yu Liu, Qixiang Pang, Shan Du
First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain.
no code implementations • 18 Nov 2022 • Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu, Yu Liu, Shan Du
In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e. g., 4x and 8x).
no code implementations • 5 Nov 2022 • Yongduo Sui, Xiang Wang, Jiancan Wu, An Zhang, Xiangnan He
Existing strategies of graph invariant learning and data augmentation suffer from limited environments or unstable causal features, which greatly limits their generalization ability on covariate shift.
no code implementations • 2 Nov 2022 • Yixuan Pei, Zhiwu Qing, Jun Cen, Xiang Wang, Shiwei Zhang, Yaxiong Wang, Mingqian Tang, Nong Sang, Xueming Qian
The former is to reduce the memory cost by preserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensing stage.
no code implementations • 24 Oct 2022 • Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels.
no code implementations • 20 Oct 2022 • Wei zhang, Jiaxi Cao, Xiang Wang, Enqi Tian, Bin Li
In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality.
1 code implementation • 20 Oct 2022 • An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua
Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.
no code implementations • 7 Oct 2022 • Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge
Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets.
no code implementations • 6 Oct 2022 • Xiang Wang, Kai Wang, Xiaohong Li, Shiguo Lian
To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties.
no code implementations • 3 Oct 2022 • Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge
Monotonic linear interpolation (MLI) - on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic - is a phenomenon that is commonly observed in the training of neural networks.
2 code implementations • 26 Jul 2022 • Yicong Li, Xiang Wang, Junbin Xiao, Tat-Seng Chua
Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene.
1 code implementation • 24 Jul 2022 • Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Xiang Wang, Yuehuan Wang, Yiliang Lv, Changxin Gao, Nong Sang
Inspired by this, we propose propose Masked Action Recognition (MAR), which reduces the redundant computation by discarding a proportion of patches and operating only on a part of the videos.
Ranked #7 on
Action Recognition
on Something-Something V2
no code implementations • 18 Jul 2022 • Amir H. Ashouri, Mostafa Elhoushi, Yuzhe Hua, Xiang Wang, Muhammad Asif Manzoor, Bryan Chan, Yaoqing Gao
This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner.
no code implementations • 18 Jun 2022 • Xiang Wang, Huaxin Zhang, Shiwei Zhang, Changxin Gao, Yuanjie Shao, Nong Sang
This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge.
1 code implementation • 16 Jun 2022 • Sihang Li, Xiang Wang, An Zhang, Yingxin Wu, Xiangnan He, Tat-Seng Chua
Specifically, without supervision signals, RGCL uses a rationale generator to reveal salient features about graph instance-discrimination as the rationale, and then creates rationale-aware views for contrastive learning.
1 code implementation • CVPR 2022 • Yicong Li, Xiang Wang, Junbin Xiao, Wei Ji, Tat-Seng Chua
At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer.
1 code implementation • 1 Jun 2022 • Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua
Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively.
no code implementations • 31 May 2022 • Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
no code implementations • 30 May 2022 • Ye Zheng, Xiang Wang, Yu Qi, Wei Li, Liwei Wu
From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.
no code implementations • 3 May 2022 • Zhenguang Liu, Sifan Wu, Chejian Xu, Xiang Wang, Lei Zhu, Shuang Wu, Fuli Feng
3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands.
no code implementations • 2 May 2022 • Xiaohong Li, Xiang Wang, Kai Wang, Shiguo Lian
Generating synchronized and natural lip movement with speech is one of the most important tasks in creating realistic virtual characters.
1 code implementation • CVPR 2022 • Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, Nong Sang
To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.
1 code implementation • 26 Apr 2022 • Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang
In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.
1 code implementation • 23 Apr 2022 • Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, Tat-Seng Chua
Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations.
no code implementations • 19 Apr 2022 • Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang
At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth.
no code implementations • CVPR 2022 • Zhiwu Qing, Shiwei Zhang, Ziyuan Huang, Yi Xu, Xiang Wang, Mingqian Tang, Changxin Gao, Rong Jin, Nong Sang
In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos.
1 code implementation • CVPR 2022 • Zhenguang Liu, Runyang Feng, Haoming Chen, Shuang Wu, Yixing Gao, Yunjun Gao, Xiang Wang
State-of-the-art methods strive to incorporate additional visual evidences from neighboring frames (supporting frames) to facilitate the pose estimation of the current frame (key frame).
1 code implementation • CVPR 2022 • Jiawei Zhang, Xiang Wang, Xiao Bai, Chen Wang, Lei Huang, Yimin Chen, Lin Gu, Jun Zhou, Tatsuya Harada, Edwin R. Hancock
The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.
1 code implementation • ICLR 2022 • Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua
Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -- rationale -- which guides the model prediction.
no code implementations • 21 Jan 2022 • Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, Tat-Seng Chua
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
1 code implementation • 14 Jan 2022 • Zhiyuan Liu, Yixin Cao, Fuli Feng, Xiang Wang, Jie Tang, Kenji Kawaguchi, Tat-Seng Chua
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).
no code implementations • 7 Jan 2022 • Jiancan Wu, Xiang Wang, Xingyu Gao, Jiawei Chen, Hongcheng Fu, Tianyu Qiu, Xiangnan He
Nonetheless, limited studies use sampled softmax loss as the learning objective to train the recommender.
1 code implementation • 30 Dec 2021 • Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, Tat-Seng Chua
To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.
1 code implementation • NeurIPS 2021 • Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
A performant paradigm towards multi-grained explainability is until-now lacking and thus a focus of our work.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
3 code implementations • 15 Nov 2021 • Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, Liwei Wu
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
Ranked #6 on
Anomaly Detection
on MVTec AD
(using extra training data)
Unsupervised Anomaly Detection
Weakly Supervised Defect Detection
no code implementations • 10 Nov 2021 • Yi Lin, Jianchao Su, Xiang Wang, Xiang Li, Jingen Liu, Kwang-Ting Cheng, Xin Yang
We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.
1 code implementation • ICLR 2022 • Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge
Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training.
1 code implementation • 11 Oct 2021 • Xiang Wang, Xinlei Chen, Simon S. Du, Yuandong Tian
Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image.
no code implementations • 9 Oct 2021 • Ye Zheng, Xiang Wang, Rui Deng, Tianpeng Bao, Rui Zhao, Liwei Wu
To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.
Ranked #27 on
Anomaly Detection
on MVTec AD
(using extra training data)
no code implementations • 29 Sep 2021 • Yongduo Sui, Xiang Wang, Tianlong Chen, Xiangnan He, Tat-Seng Chua
In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity.
1 code implementation • 5 Aug 2021 • Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, Haiyong Xie
In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations.
1 code implementation • 2 Aug 2021 • Yanfang Wang, Yongduo Sui, Xiang Wang, Zhenguang Liu, Xiangnan He
We get inspirations from the recently proposed lottery ticket hypothesis (LTH), which argues that the dense and over-parameterized model contains a much smaller and sparser sub-model that can reach comparable performance to the full model.
1 code implementation • 1 Aug 2021 • Zunlei Feng, Lechao Cheng, Xinchao Wang, Xiang Wang, Yajie Liu, Xiangtong Du, Mingli Song
To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.
1 code implementation • 12 Jul 2021 • Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan Li, Xuanping Li, Tat-Seng Chua
It aims to maximize the mutual dependencies between item content and collaborative signals.
no code implementations • 24 Jun 2021 • Zhiwu Qing, Xiang Wang, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Nong Sang
Temporal action localization aims to localize starting and ending time with action category.
1 code implementation • ICCV 2021 • Xiang Wang, Shiwei Zhang, Zhiwu Qing, Yuanjie Shao, Zhengrong Zuo, Changxin Gao, Nong Sang
Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.
Ranked #4 on
Online Action Detection
on TVSeries
1 code implementation • 20 Jun 2021 • Xiang Wang, Zhiwu Qing, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Nong Sang
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #1 on
Temporal Action Localization
on ActivityNet-1.3
(using extra training data)
no code implementations • 20 Jun 2021 • Xiang Wang, Zhiwu Qing, Ziyuan Huang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Yuanjie Shao, Nong Sang
Then our proposed Local-Global Background Modeling Network (LGBM-Net) is trained to localize instances by using only video-level labels based on Multi-Instance Learning (MIL).
Weakly-supervised Learning
Weakly-supervised Temporal Action Localization
+1
1 code implementation • 17 Jun 2021 • Zhenguang Liu, Peng Qian, Xiang Wang, Lei Zhu, Qinming He, Shouling Ji
In this paper, we explore combining deep learning with expert patterns in an explainable fashion.
no code implementations • 15 Jun 2021 • Yutong Feng, Jianwen Jiang, Ziyuan Huang, Zhiwu Qing, Xiang Wang, Shiwei Zhang, Mingqian Tang, Yue Gao
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021.
Ranked #2 on
Spatio-Temporal Action Localization
on AVA-Kinetics
(using extra training data)
1 code implementation • 13 Jun 2021 • Zhiwu Qing, Ziyuan Huang, Xiang Wang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Changxin Gao, Marcelo H. Ang Jr, Nong Sang
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
no code implementations • NeurIPS 2021 • Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou
In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems.
1 code implementation • 9 Jun 2021 • Ziyuan Huang, Zhiwu Qing, Xiang Wang, Yutong Feng, Shiwei Zhang, Jianwen Jiang, Zhurong Xia, Mingqian Tang, Nong Sang, Marcelo H. Ang Jr
In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset.
1 code implementation • 22 May 2021 • Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, Tat-Seng Chua
In this work, we scrutinize the cause-effect factors for bias amplification, identifying the main reason lies in the confounder effect of imbalanced item distribution on user representation and prediction score.
1 code implementation • 27 Apr 2021 • Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.
no code implementations • 12 Apr 2021 • An Zhang, Xiang Wang, Chengfang Fang, Jie Shi, Tat-Seng Chua, Zehua Chen
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
1 code implementation • CVPR 2021 • Xiang Wang, Shiwei Zhang, Zhiwu Qing, Yuanjie Shao, Changxin Gao, Nong Sang
In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation.
Ranked #2 on
Semi-Supervised Action Detection
on THUMOS' 14
Self-Supervised Learning
Semi-Supervised Action Detection
+1
1 code implementation • CVPR 2021 • Zhiwu Qing, Haisheng Su, Weihao Gan, Dongliang Wang, Wei Wu, Xiang Wang, Yu Qiao, Junjie Yan, Changxin Gao, Nong Sang
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #3 on
Temporal Action Localization
on ActivityNet-1.3
no code implementations • 10 Mar 2021 • Xiang Wang, Xiaoyong Li, Junxing Zhu, Zichen Xu, Kaijun Ren, Weiming Zhang, Xinwang Liu, Kui Yu
Real-world data usually have high dimensionality and it is important to mitigate the curse of dimensionality.
1 code implementation • 14 Feb 2021 • Xiang Wang, Tinglin Huang, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
no code implementations • 1 Jan 2021 • Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, Tat-Seng Chua
In this work, we focus on the causal interpretability in GNNs and propose a method, Causal Screening, from the perspective of cause-effect.
no code implementations • NeurIPS 2020 • Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge
We show that in a lazy training regime (similar to the NTK regime for neural networks) one needs at least $m = \Omega(d^{l-1})$, while a variant of gradient descent can find an approximate tensor when $m = O^*(r^{2. 5l}\log d)$.
1 code implementation • 21 Oct 2020 • Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie
In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.
1 code implementation • 7 Oct 2020 • Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, Xiangnan He
This motivates us to provide a systematic survey of existing work on RS biases.
no code implementations • 7 Aug 2020 • Xiang Wang, Changxin Gao, Shiwei Zhang, Nong Sang
By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations.
2 code implementations • 3 Jul 2020 • Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua
Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations.
no code implementations • 1 Jul 2020 • Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, Tat-Seng Chua
Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference.
1 code implementation • 30 Jun 2020 • Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective.
1 code implementation • 13 Jun 2020 • Xiang Wang, Baiteng Ma, Zhiwu Qing, Yongpeng Sang, Changxin Gao, Shiwei Zhang, Nong Sang
In this report, we present our solution for the task of temporal action localization (detection) (task 1) in ActivityNet Challenge 2020.
no code implementations • 13 Jun 2020 • Zhiwu Qing, Xiang Wang, Yongpeng Sang, Changxin Gao, Shiwei Zhang, Nong Sang
This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020. The goal of our task is to locate the start time and end time of the action in the untrimmed video, and predict action category. Firstly, we utilize the video-level feature information to train multiple video-level action classification models.
1 code implementation • 26 May 2020 • Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua
Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.
1 code implementation • 15 May 2020 • Xiaoxiao Li, Xiaopeng Guo, Liye Mei, Mingyu Shang, Jie Gao, Maojing Shu, Xiang Wang
The core of VP model is to decompose the light source into light intensity and light spatial distribution to describe the perception process of HVS, offering refinement estimation of illumination and reflectance.
1 code implementation • 21 Mar 2020 • Kun Xiao, Shaochang Tan, Guohui Wang, Xueyan An, Xiang Wang, Xiangke Wang
A customizable multi-rotor UAVs simulation platform based on ROS, Gazebo and PX4 is presented.
Robotics
1 code implementation • 12 Mar 2020 • Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua
Properly handling missing data is a fundamental challenge in recommendation.
no code implementations • 19 Feb 2020 • Xiang Wang, Sifei Liu, Huimin Ma, Ming-Hsuan Yang
In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
1 code implementation • 10 Feb 2020 • Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang
We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
11 code implementations • 6 Feb 2020 • Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Ranked #5 on
Recommendation Systems
on Yelp2018
1 code implementation • 30 Jan 2020 • Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie
The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.
no code implementations • 22 Oct 2019 • Xiang Wang, Tie Liu
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades.
no code implementations • 5 Aug 2019 • Jie Lin, Dan-Bo Zhang, Shuo Zhang, Xiang Wang, Tan Li, Wan-su Bao
We also incorporate kernel methods into the above quantum algorithms, which uses both exponential growth Hilbert space of qubits and infinite dimensionality of continuous variable for quantum feature maps.
1 code implementation • NeurIPS 2019 • Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge
Mode connectivity is a surprising phenomenon in the loss landscape of deep nets.
16 code implementations • 20 May 2019 • Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua
Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
Ranked #6 on
Link Prediction
on MovieLens 25M
7 code implementations • 20 May 2019 • Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Ranked #2 on
Link Prediction
on Yelp
no code implementations • 1 May 2019 • Rong Ge, Zhize Li, Wei-Yao Wang, Xiang Wang
Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective.
no code implementations • 26 Apr 2019 • Xiang Wang, Kai Wang, Shiguo Lian
The quality and size of training set have great impact on the results of deep learning-based face related tasks.
1 code implementation • 17 Feb 2019 • Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua
In this paper, we jointly learn the model of recommendation and knowledge graph completion.
Ranked #1 on
Knowledge Graph Completion
on DBbook2014
no code implementations • 25 Dec 2018 • Kai Wang, Yimin Lin, Luowei Wang, Liming Han, Minjie Hua, Xiang Wang, Shiguo Lian, Bill Huang
This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics.
2 code implementations • 12 Nov 2018 • Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, Tat-Seng Chua
Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest.
1 code implementation • 11 Nov 2018 • Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong
In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items.
no code implementations • ICLR 2019 • Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
We give a new algorithm for learning a two-layer neural network under a general class of input distributions.
3 code implementations • 25 Sep 2018 • Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua
Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
1 code implementation • 12 Aug 2018 • Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua
In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering.
1 code implementation • 12 Aug 2018 • Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, Hui Huang
The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.
Graphics
no code implementations • CVPR 2018 • Xiang Wang, ShaoDi You, Xi Li, Huimin Ma
Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks.
General Classification
Weakly supervised Semantic Segmentation
+1
no code implementations • 10 Jun 2017 • Xiang Wang, Xiangnan He, Liqiang Nie, Tat-Seng Chua
In this work, we address the problem of cross-domain social recommendation, i. e., recommending relevant items of information domains to potential users of social networks.
Ranked #2 on
Recommendation Systems
on Epinions
no code implementations • 7 Apr 2017 • Amit Dhurandhar, Margareta Ackerman, Xiang Wang
Clustering is a widely-used data mining tool, which aims to discover partitions of similar items in data.
no code implementations • 29 Aug 2016 • Xiang Wang, Huimin Ma, Xiaozhi Chen, ShaoDi You
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
no code implementations • 13 Oct 2015 • Xiang Wang, Ronald D. Haynes, Qihong Feng
For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.
no code implementations • CVPR 2015 • Xiaozhi Chen, Huimin Ma, Xiang Wang, Zhichen Zhao
Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.
1 code implementation • 25 Jan 2012 • Xiang Wang, Buyue Qian, Ian Davidson
Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation.