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Outputs (200)

Tackling Data Bias in Painting Classification with Style Transfer (2023)
Presentation / Conference Contribution
Vijendran, M., Li, F. W., & Shum, H. P. (2023, February). Tackling Data Bias in Painting Classification with Style Transfer. Presented at VISAPP '23: 2023 International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

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.

Length is a Curse and a Blessing for Document-level Semantics (2023)
Presentation / Conference Contribution
Xiao, C., Li, Y., Hudson, G. T., Lin, C., & Al Moubayed, N. (2023, December). Length is a Curse and a Blessing for Document-level Semantics. Presented at The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore

In recent years, contrastive learning (CL) has been extensively utilized to recover sentence and document-level encoding capability from pre-trained language models. In this work, we question the length generalizability of CL-based models, i.e., thei... Read More about Length is a Curse and a Blessing for Document-level Semantics.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023, February). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. Presented at VISAPP 2023: 18th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

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.

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, October). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. Presented at ICCV23: 2023 IEEE/CVF International Conference on Computer Vision, Paris, France

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.

Anomaly Detection with Transformers in Face Anti-spoofing (2023)
Presentation / Conference Contribution
Abduh, L., Omar, L., & Ivrissimtzis, I. (2023, May). Anomaly Detection with Transformers in Face Anti-spoofing. Presented at WSGC 2023: 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2023, Plzen, Czech Republic

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.

An Element-Wise Weights Aggregation Method for Federated Learning (2023)
Presentation / Conference Contribution
Hu, Y., Ren, H., Hu, C., Deng, J., & Xie, X. (2023, December). An Element-Wise Weights Aggregation Method for Federated Learning. Presented at 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Shanghai, China

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.

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.-C., & Liang, X. (2023, October). Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model. Presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris

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.

Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings (2023)
Presentation / Conference Contribution
Garg, A., Aujla, G., & Sun, H. (2023, October). Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings. Presented at IEEE PES ISGT Europe 2023, Grenoble, France

Carbon emissions are becoming a global concern responsible for climate change. The renewable energy sources (RESs) such as wind, solar, biomass are gaining importance to reduce emissions in the energy sector. However, these sources depend highly on v... Read More about Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings.