CV

Education

• B.S. in Electircal and Computer Engineering, Ben Gurion University of the Negev, Israel, 2013
• M.S. in Electircal and Computer Engineering, Ben Gurion University of the Negev, Israel, 2015
• Ph.D in Electircal and Computer Engineering, Ben Gurion University of the Negev, Israel, 2020

Work experience

• 2024: Senior Research Scientist and Manager
  • IBM Research
  • Multimodal-AI Group
• 2023: Senior Research Scientist and Manager
  • IBM Research
  • AI-Vision Group
• 2020-2022: Research Scientist
  • IBM Research
  • AI-Vision Group
• 2016-2020: Algorithm Developer
  • XiaoYi Multimedia
• 2014-2016: Algorithm Developer
  • Applied Materials

Publications

MAEDAY: MAE for few and zero shot AnomalY-Detection

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.

FETA: Towards Specializing Foundational Models for Expert Task Applications

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.

Promptonomyvit: Multi-task prompt learning improves video transformers using synthetic scene data

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.

Incorporating structured representations into pretrained vision & language models using scene graphs

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.

The Cell Tracking Challenge: 10 years of objective benchmarking.

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</p> </li> </article> </div>

Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation

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.</p> </li> </article> </div>

Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models.

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.

Teaching structured vision & language concepts to vision & language models.

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).

Construct-vl: Data-free continual structured vl concepts learning.

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).

CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning

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).

</ul>