4 citations · 4 across the 6 of their papers we have counts for
8 papers · 1 filter
Watch and Learn: Teaching Visual In-Context Learning by Removing Textual Shortcuts
Nimrod Shabtay, Asaf Yehudai, Avihu Dekel +2
Vision-language models (VLMs) struggle with in context learning compared to their textual counterparts. Our preliminary study shows that VLMs rely on textual shortcuts and underuti…
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…
Teaching VLMs to Localize Specific Objects from In-context Examples
Sivan Doveh, Nimrod Shabtay, Wei Lin +9
Vision-Language Models (VLMs) have shown remarkable capabilities across diverse visual tasks, including image recognition, video understanding, and Visual Question Answering (VQA)…
LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content
Nimrod Shabtay, Felipe Maia Polo, Sivan Doveh +8
The large-scale training of multi-modal models on data scraped from the web has shown outstanding utility in infusing these models with the required world knowledge to perform effe…
Comparison Visual Instruction Tuning
Wei Lin, Muhammad Jehanzeb Mirza, Sivan Doveh +4
Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation. It is e…
3VL: Using Trees to Improve Vision-Language Models' Interpretability
Nir Yellinek, Leonid Karlinsky, Raja Giryes
Vision-Language models (VLMs) have proven to be effective at aligning image and text representations, producing superior zero-shot results when transferred to many downstream tasks…
Unsupervised Domain Generalization by Learning a Bridge Across Domains
Sivan Harary, Eli Schwartz, Assaf Arbelle +11
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental cap…