11 papers
CLIMP: Contrastive Language-Image Mamba Pretraining
Nimrod Shabtay, Itamar Zimerman, Eli Schwartz +1
Contrastive Language-Image Pre-training (CLIP) relies on Vision Transformers whose attention mechanism is susceptible to spurious correlations, and scales quadratically with resolu…
CARES: Context-Aware Resolution Selector for VLMs
Moshe Kimhi, Nimrod Shabtay, Raja Giryes +2
Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visual tokens ofter to 97-99% of total toke…
Balanced Thinking: Improving Chain of Thought Training in Vision Language Models
Shaked Perek, Ben Wiesel, Avihu Dekel +2
Multimodal reasoning in vision-language models (VLMs) typically relies on a two-stage process: supervised fine-tuning (SFT) and reinforcement learning (RL). In standard SFT, all to…
Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs
Nimrod Shabtay, Moshe Kimhi, Artem Spector +5
Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational efficiency: high-resolution inputs captur…
Speech Synthesis From Continuous Features Using Per-Token Latent Diffusion
Arnon Turetzky, Avihu Dekel, Nimrod Shabtay +5
We present SALAD, a zero-shot TTS autoregressive model operating over continuous speech representations. SALAD utilizes a per-token diffusion process to refine and predict continuo…
Advancing Speech Understanding in Speech-Aware Language Models with GRPO
Avishai Elmakies, Hagai Aronowitz, Nimrod Shabtay +3
In this paper, we introduce a Group Relative Policy Optimization (GRPO)-based method for training Speech-Aware Large Language Models (SALLMs) on open-format speech understanding ta…