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| Author | Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
| Title | Simple and effective localized attribute representations for zero-shot learning | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | arXiv:2006.05938
Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their semantic descriptions. Some recent papers have shown the importance of localized features together with fine-tuning the feature extractor to obtain discriminative and transferable features. However, these methods require complex attention or part detection modules to perform explicit localization in the visual space. In contrast, in this paper we propose localizing representations in the semantic/attribute space, with a simple but effective pipeline where localization is implicit. Focusing on attribute representations, we show that our method obtains state-of-the-art performance on CUB and SUN datasets, and also achieves competitive results on AWA2 dataset, outperforming generally more complex methods with explicit localization in the visual space. Our method can be implemented easily, which can be used as a new baseline for zero shot-learning. In addition, our localized representations are highly interpretable as attribute-specific heatmaps. |
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| Notes | LAMP; 600.120 | Approved | no | ||
Call Number  |
Admin @ si @ YWH2020 | Serial | 3542 | ||
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| Author | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz | ||||
| Title | Unsupervised Domain Adaptation without Source Data by Casting a BAIT | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | arXiv:2010.12427
Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods. |
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| Notes | LAMP; 600.120 | Approved | no | ||
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Admin @ si @ YWW2020 | Serial | 3539 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | Local Prediction Aggregation: A Frustratingly Easy Source-free Domain Adaptation Method | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | We propose a simple but effective source-free domain adaptation (SFDA) method. Treating SFDA as an unsupervised clustering problem and following the intuition that local neighbors in feature space should have more similar predictions than other features, we propose to optimize an objective of prediction consistency. This objective encourages local neighborhood features in feature space to have similar predictions while features farther away in feature space have dissimilar predictions, leading to efficient feature clustering and cluster assignment simultaneously. For efficient training, we seek to optimize an upper-bound of the objective resulting in two simple terms. Furthermore, we relate popular existing methods in domain adaptation, source-free domain adaptation and contrastive learning via the perspective of discriminability and diversity. The experimental results prove the superiority of our method, and our method can be adopted as a simple but strong baseline for future research in SFDA. Our method can be also adapted to source-free open-set and partial-set DA which further shows the generalization ability of our method. Code is available in this https URL. | ||||
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| Notes | LAMP; 600.147 | Approved | no | ||
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Admin @ si @ YWW2022b | Serial | 3815 | ||
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| Author | Shiqi Yang; Yaxing Wang; Kai Wang; Shangling Jui; Joost Van de Weijer | ||||
| Title | One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | In this paper, we investigate model adaptation under domain and category shift, where the final goal is to achieve
(SF-UNDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-UNDA setting, the model cannot access source data anymore during target adaptation, which aims to address data privacy concerns. We propose a novel training scheme to learn a ( +1)-way classifier to predict the source classes and the unknown class, where samples of only known source categories are available for training. Furthermore, for target adaptation, we simply adopt a weighted entropy minimization to adapt the source pretrained model to the unlabeled target domain without source data. In experiments, we show: After source training, the resulting source model can get excellent performance for ; After target adaptation, our method surpasses current UNDA approaches which demand source data during adaptation. The versatility to several different tasks strongly proves the efficacy and generalization ability of our method. When augmented with a closed-set domain adaptation approach during target adaptation, our source-free method further outperforms the current state-of-the-art UNDA method by 2.5%, 7.2% and 13% on Office-31, Office-Home and VisDA respectively. |
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| Notes | LAMP; no proj | Approved | no | ||
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Admin @ si @ YWW2022c | Serial | 3818 | ||
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| Author | Saiping Zhang, Luis Herranz, Marta Mrak, Marc Gorriz Blanch, Shuai Wan, Fuzheng Yang | ||||
| Title | PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation-and Attention-based Network | Type | Miscellaneous | ||
| Year | 2022 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms. | ||||
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| Notes | MACO; no proj | Approved | no | ||
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Admin @ si @ ZHM2022b | Serial | 3819 | ||
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| Author | Xavier Baro; Jordi Vitria | ||||
| Title | Feature Selection with Non-Parametric Mutual Information for Adaboost Learning | Type | Miscellaneous | ||
| Year | 2005 | Publication | Abbreviated Journal | ||
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| Notes | OR;HuPBA;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BaV2005a | Serial | 582 | ||
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| Author | E. Barakova; Maya Dimitrova; T. Lorents; Petia Radeva | ||||
| Title | The Web as an “Autobiographical Agent” | Type | Miscellaneous | ||
| Year | 2004 | Publication | Ch. Bussler and D. Fensel (Eds), Lecture Notes in Artificial Intelligence, vol. 3192, ISBN: 3–540–22959–0, pp. 510–519. | Abbreviated Journal | |
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| Address | Springer-Verlag | ||||
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| Notes | MILAB | Approved | no | ||
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BCNPCL @ bcnpcl @ BDL2004 | Serial | 475 | ||
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| Author | M. Bressan; David Guillamet; Jordi Vitria | ||||
| Title | Using an ICA representation of local color histograms for object recognition. | Type | Miscellaneous | ||
| Year | 2000 | Publication | Butlleti de l´ ACIA, 22:300–307. | Abbreviated Journal | |
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BGV2000 | Serial | 338 | ||
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| Author | M. Bressan; David Guillamet; Jordi Vitria | ||||
| Title | Using a local ICA Representation of High Dimensional Data for Object Recognition and Classification. | Type | Miscellaneous | ||
| Year | 2001 | Publication | Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). | Abbreviated Journal | |
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| Address | Hawaii | ||||
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BGV2001 | Serial | 75 | ||
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| Author | Ellen J.L. Brunenberg; Oriol Pujol; Bart M. Ter Haar Romeny; Petia Radeva | ||||
| Title | Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snake | Type | Miscellaneous | ||
| Year | 2006 | Publication | 9th International Conference on Medical Image Computing and Computer–Assisted Intervention (MICCAI´06), 9–16 | Abbreviated Journal | |
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| Address | Copenhagen (Denmark) | ||||
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| Notes | MILAB;HuPBA | Approved | no | ||
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BCNPCL @ bcnpcl @ BPT2006 | Serial | 767 | ||
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| Author | X. Binefa; Petia Radeva; J.A. Cortijo; J. Garcia | ||||
| Title | Contour detection and color influence in defocused environtments. | Type | Miscellaneous | ||
| Year | 1998 | Publication | Abbreviated Journal | ||
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| Notes | MILAB | Approved | no | ||
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BCNPCL @ bcnpcl @ BRC1998 | Serial | 28 | ||
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| Author | X. Binefa; J.M. Sanchez; Petia Radeva; Jordi Vitria | ||||
| Title | Linking Visual Cues and Semantic Terms Under Specific Digital Video Domains. | Type | Miscellaneous | ||
| Year | 2000 | Publication | Journal of Visual Languages and Computing, 11(3):253–271. | Abbreviated Journal | |
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| Notes | OR;MILAB;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BRS2000 | Serial | 337 | ||
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| Author | M. Bressan; Jordi Vitria | ||||
| Title | Feature Subset Selection in an ICA Space | Type | Miscellaneous | ||
| Year | 2002 | Publication | Abbreviated Journal | ||
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BrV2002b | Serial | 277 | ||
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| Author | M. Bressan; Jordi Vitria | ||||
| Title | Improving Naive Bayes using Class Conditional ICA | Type | Miscellaneous | ||
| Year | 2002 | Publication | F. Garijo, J. Riquelme, M. Toro, (Eds.), Advances in Artificial Intelligece–Iberamia, LNAI 2527: 1–10, Springer Verlag Series. | Abbreviated Journal | |
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BrV2002d | Serial | 278 | ||
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| Author | M. Bressan; Jordi Vitria | ||||
| Title | Improving Naive Bayes using Class Condicitonal ICA. | Type | Miscellaneous | ||
| Year | 2002 | Publication | Iberoamerican Conference on Artificial Intelligence IBERAMIA 2002. | Abbreviated Journal | |
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| Address | Sevilla, Espanya | ||||
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| Notes | OR;MV | Approved | no | ||
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BCNPCL @ bcnpcl @ BrV2002e | Serial | 305 | ||
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