Author name cluster

Hua Chai

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

ICLR Conference 2023 Conference Paper

Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution

Chao Chen 0016
Haoyu Geng
Gang Zeng
Zhaobing Han
Hua Chai
Xiaokang Yang 0001
Junchi Yan

Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph, then learn structural graph properties to recover the function from its values on certain vertices --- the problem of graph signal sampling. We propose a class of regularization functionals that takes into account discrete random label noise in the graph vertex domain, then develop the GS-IMC approach which biases the reconstruction towards functions that vary little between adjacent vertices for noise reduction. Theoretical result shows that accurate reconstructions can be achieved under mild conditions. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain and builds upon a prediction-correction update algorithm to obtain the unbiased and minimum-variance reconstruction. Both GS-IMC and BGS-IMC have closed-form solutions and thus are highly scalable in large data. Experiments show that our methods achieve state-of-the-art performance on public benchmarks.

Details

AAAI Conference 2020 Conference Paper

An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

Sicheng Zhao
Yunsheng Ma
Yang Gu
Jufeng Yang
Tengfei Xing
Pengfei Xu
Runbo Hu
Hua Chai

Emotion recognition in user-generated videos plays an important role in human-centered computing. Existing methods mainly employ traditional two-stage shallow pipeline, i. e. extracting visual and/or audio features and training classiﬁers. In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs). Speciﬁcally, we develop a deep Visual- Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classiﬁcation loss, i. e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https: //github. com/maysonma/VAANet.

PDF Details

IROS Conference 2020 Conference Paper

ARPDR: An Accurate and Robust Pedestrian Dead Reckoning System for Indoor Localization on Handheld Smartphones

Xiaoqiang Teng
Pengfei Xu 0013
Deke Guo
Yulan Guo
Runbo Hu
Hua Chai
Didi Chuxing

The proliferation of mobile computing has prompted Pedestrian Dead Reckoning (PDR) to be one of the most attractive and promising indoor localization techniques for ubiquitous applications. The existing PDR approaches either suffer position drifts caused by accumulative errors or are sensitive to various users. This paper presents ARPDR, an accurate and robust PDR approach to improve the accuracy and robustness of indoor localization methods. Particularly, we propose a novel step counting algorithm based on motion models by deeply exploiting inertial sensor data. We then combine step counting with adaptive thresholding to personalize the PDR system for different users. Furthermore, we propose a novel stride-heading model with a deep neural network to predict stride lengths and walking orientations, thus the displacement errors are significantly reduced. Extensive experiments on public datasets demonstrate that ARPDR outperforms the state-of-the-art PDR methods.

Details

AAAI Conference 2020 Conference Paper

Multi-Source Distilling Domain Adaptation

Sicheng Zhao
Guangzhi Wang
Shanghang Zhang
Yang Gu
Yaxian Li
Zhichao Song
Pengfei Xu
Runbo Hu

Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods usually assume that the labeled data is sampled from a single source distribution. However, in practice, labeled data may be collected from multiple sources, while naive application of the single-source DA algorithms may lead to suboptimal solutions. In this paper, we propose a novel multi-source distilling domain adaptation (MDDA) network, which not only considers the different distances among multiple sources and the target, but also investigates the different similarities of the source samples to the target ones. Speciﬁcally, the proposed MDDA includes four stages: (1) pre-train the source classi- ﬁers separately using the training data from each source; (2) adversarially map the target into the feature space of each source respectively by minimizing the empirical Wasserstein distance between source and target; (3) select the source training samples that are closer to the target to ﬁne-tune the source classiﬁers; and (4) classify each encoded target feature by corresponding source classiﬁer, and aggregate different predictions using respective domain weight, which corresponds to the discrepancy between each source and target. Extensive experiments are conducted on public DA benchmarks, and the results demonstrate that the proposed MDDA signiﬁcantly outperforms the state-of-the-art approaches. Our source code is released at: https: //github. com/daoyuan98/MDDA.

PDF Details

AAAI Conference 2019 Conference Paper

Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning

Ji Zhao
Dan Peng
Chuhan Wu
Huan Chen
Meiyu Yu
Wanji Zheng
Li Ma
Hua Chai

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

PDF Details

NeurIPS Conference 2019 Conference Paper

Multi-source Domain Adaptation for Semantic Segmentation

Sicheng Zhao
Bo Li
Xiangyu Yue
Yang Gu
Pengfei Xu
Runbo Hu
Hua Chai
Kurt Keutzer

Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a single-source setting, which cannot easily handle a more practical scenario of multiple sources with different distributions. In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation. Specifically, we design a novel framework, termed Multi-source Adversarial Domain Aggregation Network (MADAN), which can be trained in an end-to-end manner. First, we generate an adapted domain for each source with dynamic semantic consistency while aligning at the pixel-level cycle-consistently towards the target. Second, we propose sub-domain aggregation discriminator and cross-domain cycle discriminator to make different adapted domains more closely aggregated. Finally, feature-level alignment is performed between the aggregated domain and target domain while training the segmentation network. Extensive experiments from synthetic GTA and SYNTHIA to real Cityscapes and BDDS datasets demonstrate that the proposed MADAN model outperforms state-of-the-art approaches. Our source code is released at: https: //github. com/Luodian/MADAN.

PDF Details