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Results: 12
Number of items: 12
  • Das, P., Baslamisli, A. S., Le, H. A., Karaoglu, S., & Gevers, T. (2024, October 29). ShadingNet Dataset Release [Data set]. Universiteit van Amsterdam. https://doi.org/10.21942/uva.27241383.v2
  • Das, P., Gevers, M., Karaoglu, S., & Gevers, T. (2023). IDTransformer: Transformer for Intrinsic Image Decomposition. In 2023 IEEE/CVF International Conference on Computer Vision Workshops: proceedings: ICCVW 2023 : Paris, France, 2-6 October 2023 (pp. 816-825). IEEE Computer Society. https://doi.org/10.1109/ICCVW60793.2023.00089
  • Das, P., Karaoğlu, S., Gijsenij, A., & Gevers, T. (2023). SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes. In L. Karlinsky, T. Michaeli, & K. Nishino (Eds.), Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022 : proceedings (Vol. III, pp. 605-620). (Lecture Notes in Computer Science; Vol. 13803). Springer. https://doi.org/10.1007/978-3-031-25066-8_35
  • Open Access
    Das, P. (2023). Photometric invariance for intrinsic image decomposition. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Das, P., Karaoglu, S., & Gevers, T. (2022). PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: New Orleans, Louisiana, 19-24 June 2022 : proceedings (pp. 19758-19767). (CVPR). IEEE Computer Society. https://doi.org/10.1109/CVPR52688.2022.01917
  • Open Access
    Das, P., Karaoglu, S., & Gevers, T. (2022). Intrinsic image decomposition using physics-based cues and CNNs. Computer Vision and Image Understanding, 223, Article 103538. https://doi.org/10.1016/j.cviu.2022.103538
  • Lê, H.-A., Mensink, T., Das, P., Karaoglu, S., & Gevers, T. (2021). EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 1578-1588). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00162
  • Open Access
    Das, P., Liu, Y., Karaoglu, S., & Gevers, T. (2021). Generative Models for Multi-Illumination Color Constancy. In 2021 IEEE/CVF International Conference on Computer Vision Workshops: proceedings : ICCVW 2021 : 11-17 October 2021, virtual event (pp. 1194-1203 ). IEEE Computer Society. https://doi.org/10.1109/ICCVW54120.2021.00139
  • Open Access
    Baslamisli, A. S., Das, P., Lê, H.-A., Karaoglu, S., & Gevers, T. (2021). ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition. International Journal of Computer Vision, 129(8), 2445–2473. https://doi.org/10.1007/s11263-021-01477-5
  • Peeters, S., Hagen, S., & Das, P. (2020, February 20). Salvaging the Internet Hate Machine: Using the discourse of extremist online subcultures to identify emergent extreme speech [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3676482
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