Hila Chefer
Hi there! I am a Ph.D. 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 Google Research in Tel Aviv.
My research is centered around computer vision and multi-modal learning. I am particularly passionate about developing methods to understand deep neural networks and leveraging their insights to enhance the expressiveness, robustness, and fairness of the model.
Google Scholar /
Twitter /
GitHub /
Semantic Scholar /
LinkedIn
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Selected Publications
* indicates equal contribution.
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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
arxiv, 2024
Project page
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Paper
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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
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Paper
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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
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Paper
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Optimizing Relevance Maps of Vision Transformers Improves Robustness
Hila Chefer,
Idan Schwartz,
Lior Wolf
NeurIPS, 2022
Project page
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Paper
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Image-Based Clip-Guided Essence Transfer
Hila Chefer,
Sagie Benaim,
Roni Paiss,
Lior Wolf
ECCV, 2022
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Paper
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No Token Left Behind: Explainability-Aided Image Classification and Generation
Roni Paiss,
Hila Chefer,
Lior Wolf
ECCV, 2022
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Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
Hila Chefer,
Shir Gur,
Lior Wolf
ICCV, 2021 (Oral)
Project page
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Paper
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Transformer Interpretability Beyond Attention Visualization
Hila Chefer,
Shir Gur,
Lior Wolf
CVPR, 2021
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Paper
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Lumiere: A Space-Time Diffusion Model for Video Generation
Hila Chefer
Recording
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All Things ViTs: Understanding and Interpreting Attention in Vision
Hila Chefer*,
Sayak Paul*
CVPR Tutorial, 2023
Tutorial page
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Recording
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Transformer Explainability Beyond Accountability
Hila Chefer
Columbia Vision Seminar
Recording
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Intro to Transformers and Transformer Explainability
Hila Chefer
Microsoft Data Science Bond
Recording
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Leveraging Attention for Improved Accuracy and Robustness
Hila Chefer
Voxel51 Computer Vision Meetup
Recording
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Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-image Diffusion Models
Hila Chefer
Microsoft Data Science Bond
Recording
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Contact: hilach70 at gmail dot com
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