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Outputs (187)

Anomaly Detection with Transformers in Face Anti-spoofing (2023)
Presentation / Conference Contribution
Abduh, L., Omar, L., & Ivrissimtzis, I. (2023). Anomaly Detection with Transformers in Face Anti-spoofing. . https://doi.org/10.24132/JWSCG.2023.10

Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNe... Read More about Anomaly Detection with Transformers in Face Anti-spoofing.

Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model (2023)
Presentation / Conference Contribution
Wang, Y., Leng, Z., Li, F. W. B., Wu, S., & Liang, X. (2023). Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/ICCV51070.2023.02014

Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise motion sequences, failing to effectively control the temporal and spatial... Read More about Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model.

An Element-Wise Weights Aggregation Method for Federated Learning (2023)
Presentation / Conference Contribution
Hu, Y., Ren, H., Hu, C., Deng, J., & Xie, X. (2023). An Element-Wise Weights Aggregation Method for Federated Learning. In 2023 IEEE International Conference on Data Mining Workshops (ICDMW). https://doi.org/10.1109/icdmw60847.2023.00031

Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the original device, thereby preserving privacy. A central challenge in FL... Read More about An Element-Wise Weights Aggregation Method for Federated Learning.

Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models (2023)
Presentation / Conference Contribution
Chang, Z., Findlay, E. J., Zhang, H., & Shum, H. P. (2023). Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP (64-74). https://doi.org/10.5220/0011631000003417

Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancem... Read More about Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models.

Tackling Data Bias in Painting Classification with Style Transfer (2023)
Presentation / Conference Contribution
Vijendran, M., Li, F. W., & Shum, H. P. (2023). Tackling Data Bias in Painting Classification with Style Transfer. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP (250-261). https://doi.org/10.5220/0011776600003417

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transf... Read More about Tackling Data Bias in Painting Classification with Style Transfer.

Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers (2023)
Presentation / Conference Contribution
Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. In ICCV '23: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. https://doi.org/10.1109/ICCV51070.2023.01341

Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks... Read More about Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers.

Sorting and Hypergraph Orientation under Uncertainty with Predictions (2023)
Presentation / Conference Contribution
Erlebach, T., de Lima, M., Megow, N., & Schlöter, J. (2023). Sorting and Hypergraph Orientation under Uncertainty with Predictions. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (5577-5585). https://doi.org/10.24963/ijcai.2023/619

Learning-augmented algorithms have been attracting increasing interest, but have only recently been considered in the setting of explorable uncertainty where precise values of uncertain input elements can be obtained by a query and the goal is to min... Read More about Sorting and Hypergraph Orientation under Uncertainty with Predictions.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. . https://doi.org/10.5220/0011684700003417

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal clas... Read More about Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption.

Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient (2023)
Presentation / Conference Contribution
Lu, Z., Wang, H., Chang, Z., Yang, G., & Shum, H. P. (2023). Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient. . https://doi.org/10.1109/ICCV51070.2023.00424

Recently, methods for skeleton-based human activity recognition have been shown to be vulnerable to adversarial attacks. However, these attack methods require either the full knowledge of the victim (i.e. white-box attacks), access to training data (... Read More about Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient.