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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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publications
MAEDAY: MAE for few and zero shot AnomalY-Detection
Published in ArXiv, 2022
Recommended citation: Schwartz, E., Arbelle, A., Karlinsky, L., Harary, S., Scheidegger, F., Doveh, S. and Giryes, R., 2022. MAEDAY: MAE for few and zero shot AnomalY-Detection. arXiv preprint arXiv:2211.14307. https://arxiv.org/pdf/2211.14307
FETA: Towards Specializing Foundational Models for Expert Task Applications
Published in NuerIPS, 2022
Recommended citation: Alfassy, A., Arbelle, A., Halimi, O., Harary, S., Herzig, R., Schwartz, E., Panda, R., Dolfi, M., Auer, C., Staar, P. and Saenko, K., 2022. FETA: Towards Specializing Foundational Models for Expert Task Applications. Advances in Neural Information Processing Systems, 35, pp.29873-29888. https://proceedings.neurips.cc/paper_files/paper/2022/file/c12dd3034259fc000d80db823041c187-Paper-Datasets_and_Benchmarks.pdf
Promptonomyvit: Multi-task prompt learning improves video transformers using synthetic scene data
Published in ArXiv, 2022
Recommended citation: Herzig, R., Abramovich, O., Ben-Avraham, E., Arbelle, A., Karlinsky, L., Shamir, A., Darrell, T. and Globerson, A., 2022. Promptonomyvit: Multi-task prompt learning improves video transformers using synthetic scene data. arXiv preprint arXiv:2212.04821. https://arxiv.org/pdf/2212.04821
Incorporating structured representations into pretrained vision & language models using scene graphs
Published in ArXiv, 2023
Recommended citation: Herzig, R., Mendelson, A., Karlinsky, L., Arbelle, A., Feris, R., Darrell, T. and Globerson, A., 2023. "Incorporating structured representations into pretrained vision & language models using scene graphs." arXiv preprint arXiv:2305.06343. https://arxiv.org/pdf/2305.06343
The Cell Tracking Challenge: 10 years of objective benchmarking.
Published in Nature Methods, 2023
The Cell Tracking Challenge is an ongoing benchmarking.
Recommended citation: Maška, M., Ulman, V., Delgado-Rodriguez, P., Gómez-de-Mariscal, E., Nečasová, T., Guerrero Peña, F.A., Ren, T.I., Meyerowitz, E.M., Scherr, T., Löffler, K. and Mikut, R., 2023. "The Cell Tracking Challenge: 10 years of objective benchmarking." Nature Methods, pp.1-11 https://www.nature.com/articles/s41592-023-01879-y</p> </article> </div> Published in IEEE Transactions on Pattern Analysis and Machine Intelligence., 2023 Constructing a differentiable histogram loss function with application to image-to-image translation. Recommended citation: Avi-Aharon, M., Arbelle, A. and Raviv, T.R., 2023. "Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation." IEEE Transactions on Pattern Analysis and Machine Intelligence. https://ieeexplore.ieee.org/iel7/34/4359286/10133915.pdf</p> </article> </div> Published in ArXiv, 2023 Recommended citation: Doveh, S., Arbelle, A., Harary, S., Alfassy, A., Herzig, R., Kim, D., Giryes, R., Feris, R., Panda, R., Ullman, S. and Karlinsky, L., 2023. "Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models." ArXiv preprint arXiv:2305.19595. https://arxiv.org/pdf/2305.19595 Published in CVPR, 2023 Recommended citation: Doveh, S., Arbelle, A., Harary, S., Schwartz, E., Herzig, R., Giryes, R., Feris, R., Panda, R., Ullman, S. and Karlinsky, L., 2023. Teaching structured vision & language concepts to vision & language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2657-2668). https://openaccess.thecvf.com/content/CVPR2023/papers/Doveh_Teaching_Structured_Vision__Language_Concepts_to_Vision__Language_CVPR_2023_paper.pdf Published in CVPR, 2023 Recommended citation: Smith, J.S., Cascante-Bonilla, P., Arbelle, A., Kim, D., Panda, R., Cox, D., Yang, D., Kira, Z., Feris, R. and Karlinsky, L., 2023. Construct-vl: Data-free continual structured vl concepts learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14994-15004). https://openaccess.thecvf.com/content/CVPR2023/papers/Smith_ConStruct-VL_Data-Free_Continual_Structured_VL_Concepts_Learning_CVPR_2023_paper.pdf Published in CVPR, 2023 Recommended citation: Smith, J.S., Karlinsky, L., Gutta, V., Cascante-Bonilla, P., Kim, D., Arbelle, A., Panda, R., Feris, R. and Kira, Z., 2023. CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11909-11919). https://openaccess.thecvf.com/content/CVPR2023/papers/Smith_CODA-Prompt_COntinual_Decomposed_Attention-Based_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2023_paper.pdf Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models.
Teaching structured vision & language concepts to vision & language models.
Construct-vl: Data-free continual structured vl concepts learning.
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning