Showing 14 research items.
Conference Paper
Sarode*, S., Khan*, M. S. U., Shehzadi, T., Stricker, D., Afzal M. Z. (2025). Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies.
Sinha, S., Khan, M. S. U., Sheikh, T. U., Stricker, D., Afzal M. Z. (2024). CICA: Content-Injected Contrastive Alignment for Zero-Shot Document Image Classification.
Journal Paper
Khan, M. S. U., Afzal, M. Z., Stricker D. (2025). SituationalLLM: Proactive Language Models with Scene Awareness for Dynamic, Contextual Task Guidance [version 1; peer review: awaiting peer review].
Khan, M. S. U., Sinha, S., Stricker, D., Liwicki, M., Afzal M. Z. (2024). Shape2.5D: A Dataset of Texture-less Surfaces for Depth and Normals Estimation.
Khan, M. S. U., Pagani, A., Liwicki, M., Stricker, D., Afzal M. Z. (2022). Three-Dimensional Reconstruction from a Single RGB Image using Deep Learning: A Review.
Preprint
Khan, M. S. U., Khan, M. A. U., Stricker, D., Afzal M. Z. (2024). Continual Human Pose Estimation for Incremental Integration of Keypoints and Pose Variations.
Khan, M. S. U., Limbachiya, D., Stricker, D., Afzal M. Z. (2024). Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach.
Khan, M. S. U., Naeem, M. F., Tombari, F., Gool, L. V., Stricker, D., Afzal M. Z. (2024). FocusCLIP: Multimodal Subject-Level Guidance for Zero-Shot Transfer in Human-Centric Tasks.
Khan M. S. U. (2021). BCSD: A novel segmentation dataset for signatures on bank checks.
Report
Khan, M. S. U., Afzal M. Z. (2021). Depth Reconstruction of Low-Texture Surfaces from a Single View.
Khan, M. S. U., Taetz B. (2021). Investigate Cloth Artefacts of Loosely Coupled Sensor Networks with TailorNet.
Thesis
Khan, M. S. U., Afzal, M. Z., Stricker D. (2022). Learning to Reconstruct Textureless and Transparent Surfaces in 3D.
Khan, M. S. U., Tariq, M. M., Ahmad B. (2018). Signature Verification.
Workshop Paper
Khan*, M. S. U., Shehzadi*, T., Noor, R., Stricker, D., Afzal M. Z. (2024). Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification.