7 papers
Compositional Video Generation via Inference-Time Guidance
Ariel Shaulov, Eitan Shaar, Amit Edenzon +2
Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relations between entities, attribut…
Latent Transfer Attack: Adversarial Examples via Generative Latent Spaces
Eitan Shaar, Ariel Shaulov, Yalcin Tur +2
Adversarial attacks are a central tool for probing the robustness of modern vision models, yet most methods optimize perturbations directly in pixel space under or $\…
TokenTrim: Inference-Time Token Pruning for Autoregressive Long Video Generation
Ariel Shaulov, Eitan Shaar, Amit Edenzon +1
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 t…
FlowMo: Variance-Based Flow Guidance for Coherent Motion in Video Generation
Ariel Shaulov, Itay Hazan, Lior Wolf +1
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 thi…
Adapting to the Unknown: Training-Free Audio-Visual Event Perception with Dynamic Thresholds
Eitan Shaar, Ariel Shaulov, Gal Chechik +1
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 appr…
Classifier-Guided Captioning Across Modalities
Ariel Shaulov, Tal Shaharabany, Eitan Shaar +2
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 gen…
Zero-Shot Audio Captioning via Audibility Guidance
Tal Shaharabany, Ariel Shaulov, Lior Wolf
The task of audio captioning is similar in essence to tasks such as image and video captioning. However, it has received much less attention. We propose three desiderata for captio…