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  • Association for the Advancement of Artificial Intelligence (AAAI)  (7)
  • Lu, Ke  (7)
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  • Association for the Advancement of Artificial Intelligence (AAAI)  (7)
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  • 1
    Online Resource
    Online Resource
    Cross-Domain Adaptative Learning for Online Advertisement Customer Lifetime Value Prediction
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2023
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 37, No. 4 ( 2023-06-26), p. 4605-4613
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 37, No. 4 ( 2023-06-26), p. 4605-4613
    Abstract: Accurate estimation of customer lifetime value (LTV), which reflects the potential consumption of a user over a period of time, is crucial for the revenue management of online advertising platforms. However, predicting LTV in real-world applications is not an easy task since the user consumption data is usually insufficient within a specific domain. To tackle this problem, we propose a novel cross-domain adaptative framework (CDAF) to leverage consumption data from different domains. The proposed method is able to simultaneously mitigate the data scarce problem and the distribution gap problem caused by data from different domains. To be specific, our method firstly learns a LTV prediction model from a different but related platform with sufficient data provision. Subsequently, we exploit domain-invariant information to mitigate data scarce problem by minimizing the Wasserstein discrepancy between the encoded user representations of two domains. In addition, we design a dual-predictor schema which not only enhances domain-invariant information in the semantic space but also preserves domain-specific information for accurate target prediction. The proposed framework is evaluated on five datasets collected from real historical data on the advertising platform of Tencent Games. Experimental results verify that the proposed framework is able to significantly improve the LTV prediction performance on this platform. For instance, our method can boost DCNv2 with the improvement of 13.7% in terms of AUC on dataset G2. Code: https://github.com/TL-UESTC/CDAF.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v37i4.25583
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2023
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Balanced Open Set Domain Adaptation via Centroid Alignment
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, No. 9 ( 2021-05-18), p. 8013-8020
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35, No. 9 ( 2021-05-18), p. 8013-8020
    Abstract: Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA methods. To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples. Specifically, to reduce the domain gaps, we first project the features to a hyperspherical latent space. In this space, we propose to bound the centroid deviation angles to not only increase the intra-class compactness but also enlarge the inter-class margins. With the bounded centroid deviation angles, we employ the statistical Extreme Value Theory to recognize the unknown samples that are misclassified into known classes. In addition, to learn better centroids, we propose an improved centroid update strategy based on sample reweighting and adaptive update rate to cooperate with centroid alignment. Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v35i9.16977
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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    Online Resource
  • 3
    Online Resource
    Online Resource
    TransZero: Attribute-Guided Transformer for Zero-Shot Learning
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2022
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 36, No. 1 ( 2022-06-28), p. 330-338
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 36, No. 1 ( 2022-06-28), p. 330-338
    Abstract: Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which are strong prior for localization of object attribute for representing discriminative region features enabling significant visual-semantic interaction. Although few attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected. In this paper, we propose an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero. Specifically, TransZero takes a feature augmentation encoder to alleviate the cross-dataset bias between ImageNet and ZSL benchmarks and improve the transferability of visual features by reducing the entangled relative geometry relationships among region features. To learn locality-augmented visual features, TransZero employs a visual-semantic decoder to localize the most relevant image regions to each attributes from a given image under the guidance of attribute semantic information. Then, the locality-augmented visual features and semantic vectors are used for conducting effective visual-semantic interaction in a visual-semantic embedding network. Extensive experiments show that TransZero achieves a new state-of-the-art on three ZSL benchmarks. The codes are available at: https://github.com/shiming-chen/TransZero.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v36i1.19909
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2022
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Action Recognition With Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2018
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 32, No. 1 ( 2018-04-27)
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 32, No. 1 ( 2018-04-27)
    Abstract: Action recognition is an important yet challenging task in computer vision. In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams. We first introduce a coarse-to-fine network which extracts shared deep features at different action class granularities and progressively integrates them to obtain a more accurate feature representation for input actions. We further introduce an asynchronous fusion network. It fuses information from different streams by asynchronously integrating stream-wise features at different time points, hence better leveraging the complementary information in different streams. Experimental results on action recognition benchmarks demonstrate that our approach achieves the state-of-the-art performance.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v32i1.12232
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2018
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  • 5
    Online Resource
    Online Resource
    Exploring Visual Context for Weakly Supervised Person Search
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2022
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 36, No. 3 ( 2022-06-28), p. 3027-3035
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 36, No. 3 ( 2022-06-28), p. 3027-3035
    Abstract: Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person re-identification. Existing approaches follow a fully supervised setting where both bounding box and identity annotations are available. However, annotating identities is labor-intensive, limiting the practicability and scalability of current frameworks. This paper inventively considers weakly supervised person search with only bounding box annotations. We propose to address this novel task by investigating three levels of context clues (i.e., detection, memory and scene) in unconstrained natural images. The first two are employed to promote local and global discriminative capabilities, while the latter enhances clustering accuracy. Despite its simple design, our CGPS boosts the baseline model by 8.8% in mAP on CUHK-SYSU. Surprisingly, it even achieves comparable performance with several supervised person search models. Our code is available at https://github. com/ljpadam/CGPS.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v36i3.20209
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    Shape-Adaptive Selection and Measurement for Oriented Object Detection
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2022
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 36, No. 1 ( 2022-06-28), p. 923-932
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 36, No. 1 ( 2022-06-28), p. 923-932
    Abstract: The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performance of the detection method and has achieved great progress. However, existing sample selection strategies still overlook some issues: (1) most of them ignore the object shape information; (2) they do not make a potential distinction between selected positive samples; and (3) some of them can only be applied to either anchor-free or anchor-based methods and cannot be used for both of them simultaneously. In this paper, we propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples. Specifically, the SA-S strategy dynamically selects samples according to the shape information and characteristics distribution of objects. The SA-M strategy measures the localization potential and adds quality information on the selected positive samples. The experimental results on both anchor-free and anchor-based baselines and four publicly available oriented datasets (DOTA, HRSC2016, UCAS-AOD, and ICDAR2015) demonstrate the effectiveness of the proposed method.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v36i1.19975
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2022
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    Online Resource
  • 7
    Online Resource
    Online Resource
    From Zero-Shot Learning to Cold-Start Recommendation
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2019
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33, No. 01 ( 2019-07-17), p. 4189-4196
    Details
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 33, No. 01 ( 2019-07-17), p. 4189-4196
    Abstract: Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    URL: Article
    DOI: 10.1609/aaai.v33i01.33014189
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2019
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