Arian Bakhtiarnia

ACM Comput. Surv., July, 2024

Are LLM-based methods good enough for detecting unfair terms of service?

[BibT_eX]

[DOI]

CoRR, 2024

Accurate Gigapixel Crowd Counting by Iterative Zooming and Refinement.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videos.

[BibT_eX]

[DOI]

CoRR, 2023

Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2023

Continual Transformers: Redundancy-Free Attention for Online Inference.

[BibT_eX]

[DOI]

Lukas Hedegaard

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Dynamic Split Computing for Efficient Deep EDGE Intelligence.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2023

Communication Efficient Model-Aware Federated Learning for Visual Crowd Counting and Density Estimation in Smart Cities.

[BibT_eX]

[DOI]

Proceedings of the 31st European Signal Processing Conference, 2023

2022

Single-layer vision transformers for more accurate early exits with less overhead.

[BibT_eX]

[DOI]

Neural Networks, 2022

Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Smart Cities Conference, 2022

2021

Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Multi-Exit Vision Transformer for Dynamic Inference.

[BibT_eX]

[DOI]