Ariel Shaulov

Compositional Video Generation via Inference-Time Guidance

Ariel Shaulov, Eitan Shaar, Amit Edenzon, Gal Chechik et autres

Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relations between entities, attributes, actions, and motion directions. We hypothesize that these failures need not be addressed by retraining the generator, but can instead be …

Compositional Video Generation via Inference-Time Guidance

Ariel Shaulov, Eitan Shaar, Amit Edenzon, Gal Chechik et autres

Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relations between entities, attributes, actions, and motion directions. We hypothesize that these failures need not be addressed by retraining the generator, but can instead be …

il, gb (code pays fourni par la source)

Latent Transfer Attack: Adversarial Examples via Generative Latent Spaces

Eitan Shaar, Ariel Shaulov, Yalcin Tur, Gal Chechik et autres

Adversarial attacks are a central tool for probing the robustness of modern vision models, yet most methods optimize perturbations directly in pixel space under $\ell_\infty$ or $\ell_2$ constraints. While effective in white-box settings, pixel-space optimization often produces high-frequency, texture-like noise that is …

Latent Transfer Attack: Adversarial Examples via Generative Latent Spaces

Eitan Shaar, Ariel Shaulov, Yalcin Tur, Gal Chechik et autres

Adversarial attacks are a central tool for probing the robustness of modern vision models, yet most methods optimize perturbations directly in pixel space under $\ell_\infty$ or $\ell_2$ constraints. While effective in white-box settings, pixel-space optimization often produces high-frequency, texture-like noise that is …

il, us, gb (code pays fourni par la source)

Safeguarding Language Models via Self-Destruct Trapdoor

Association for Computational Linguistics 2026, Bar Alon, shachar katz, Mahmood Sharif et autres

The potential misuse and misalignment of language models (LMs) is a central safety concern. This work presents Self-Destruct, a novel mechanism to restrict specific behaviors in LMs by leveraging overlooked properties of the underlying hardware. We observe that the LM frameworks use …

il (code pays fourni par la source)

TokenTrim: Inference-Time Token Pruning for Autoregressive Long Video Generation

Ariel Shaulov, Eitan Shaar, Amit Edenzon, Lior Wolf

Auto-regressive video generation enables long video synthesis by iteratively conditioning each new batch of frames on previously generated content. However, recent work has shown that such pipelines suffer from severe temporal drift, where errors accumulate and amplify over long horizons. We hypothesize …

TokenTrim: Inference-Time Token Pruning for Autoregressive Long Video Generation

Ariel Shaulov, Eitan Shaar, Amit Edenzon, Lior Wolf

Auto-regressive video generation enables long video synthesis by iteratively conditioning each new batch of frames on previously generated content. However, recent work has shown that such pipelines suffer from severe temporal drift, where errors accumulate and amplify over long horizons. We hypothesize …

Safeguarding Language Models via Self-Destruct Trapdoor

Shahar Katz, Bar Alon, Ariel Shaulov, Lior Wolf et autres

Shahar Katz, Bar Alon, Ariel Shaulov, Lior Wolf, Mahmood Sharif. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

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Adapting to the Unknown: Training-Free Audio-Visual Event Perception with Dynamic Thresholds

Eitan Shaar, Ariel Shaulov, Gal Chechik, Lior Wolf

In the domain of audio-visual event perception, which focuses on the temporal localization and classification of events across distinct modalities (audio and visual), existing approaches are constrained by the vocabulary available in their training data. This limitation significantly impedes their capacity to …

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Classifier-Guided Captioning Across Modalities

Ariel Shaulov, Tal Shaharabany, Eitan Shaar, Gal Chechik et autres

Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to generalize to other modality distributions and contexts. This limitation hinders performance in tasks like audio or video …

il (code pays fourni par la source)

Classifier-Guided Captioning Across Modalities

Ariel Shaulov, Tal Shaharabany, Eitan Shaar, Gal Chechik et autres

Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to generalize to other modality distributions and contexts. This limitation hinders performance in tasks like audio or video …

FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation

Ariel Shaulov, Itay Hazan, Lior Wolf, Hila Chefer

Text-to-video diffusion models are notoriously limited in their ability to model temporal aspects such as motion, physics, and dynamic interactions. Existing approaches address this limitation by retraining the model or introducing external conditioning signals to enforce temporal consistency. In this work, we …

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