Hila Chefer

Hi there! I am a PhD Candidate at Tel Aviv University, working in the Deep Learning Lab under the supervision of Prof. Lior Wolf. Also, I am currently a research intern at Meta AI in Tel Aviv. Before that, I was a research intern at Google. My research is centered around computer vision and multi-modal learning. I am particularly passionate about developing tools to enhance interpretability, reliability and controllability of deep foundation models, using the model's internal representations. My work has been covered by The Verge, ZDNET, Analytics India Magazine, and others.

Email / Google Scholar / Twitter / GitHub / Semantic Scholar / LinkedIn

Selected Publications

* indicates equal contribution.

	Still-Moving: Customized Video Generation without Customized Video Data Hila Chefer, Shiran Zada, Roni Paiss, Ariel Efrat, Omer Tov, Michael Rubinstein , Lior Wolf, Tali Dekel, Tomer Michaeli, Inbar Mosseri SIGGRAPH Asia (Journal), 2024 Project page / Paper
	Lumiere: A Space-Time Diffusion Model for Video Generation Omer Bartal, Hila Chefer,** Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Efrat, Junhwa Hur , Yuanzhen Li Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, Inbar Mosseri SIGGRAPH Asia, 2024* Project page / Paper
	The Hidden Language of Diffusion Models Hila Chefer, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher, Michal Irani, Inbar Mosseri, Lior Wolf ICLR, 2024 Project page / Paper
	Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or SIGGRAPH (journal), 2023 Project page / Paper
	Optimizing Relevance Maps of Vision Transformers Improves Robustness Hila Chefer, Idan Schwartz, Lior Wolf NeurIPS, 2022 Project page / Paper
	Image-Based Clip-Guided Essence Transfer Hila Chefer, Sagie Benaim, Roni Paiss, Lior Wolf ECCV, 2022 Project page / Paper
	No Token Left Behind: Explainability-Aided Image Classification and Generation Roni Paiss, Hila Chefer, Lior Wolf ECCV, 2022 Paper
	Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers Hila Chefer, Shir Gur, Lior Wolf ICCV, 2021 (Oral) Project page / Paper
	Transformer Interpretability Beyond Attention Visualization Hila Chefer, Shir Gur, Lior Wolf CVPR, 2021 Project page / Paper

Selected Talks

	All Things ViTs: Understanding and Interpreting Attention in Vision *Hila Chefer,** Sayak Paul* CVPR Tutorial, 2023 Tutorial page / Recording
	Transformer Explainability Beyond Accountability Hila Chefer Columbia Vision Seminar Recording
	Intro to Transformers and Transformer Explainability Hila Chefer Microsoft Data Science Bond Recording
	Lumiere: A Space-Time Diffusion Model for Video Generation Hila Chefer Recording
	Leveraging Attention for Improved Accuracy and Robustness Hila Chefer Voxel51 Computer Vision Meetup Recording
	Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-image Diffusion Models Hila Chefer Microsoft Data Science Bond Recording

Contact: hilach70 at gmail dot com

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