FuseCap is one the the best Image Captioning models I have tried so far🤯 The main idea is: Visual experts extract meaningful information from images. This information is then fused with the original captions by an LLM Fuser, resulting in rich captions. This ends up in a very

Thanks for the kind words! You can find more details on our paper here: openaccess.thecvf.com/content/WACV20…

Thanks AK for sharing. We are happy to announce that our work was accepted to #CVPR2025! 🥳 #CVPR2024

I am thrilled to announce that our work was accepted as SPOTLIGHT to #CVPR2025! The official code is available at github.com/amazon-science… (currently, code and checkpoints for inference. Training will be available soon). Amazon Science

🥁 🥁 🥁 Announcing our new work- Paint by Inpaint: Learning to Add Image Objects by Removing Them First Together with Navve Wasserman, Roy Ganz, and Ron Kimmel, we've developed a framework designed for adding objects to images! Paper Page: rotsteinnoam.github.io/Paint-by-Inpai… 1/7

Paint by Inpaint Learning to Add Image Objects by Removing Them First Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual instructions without

💡New research: 𝐏𝐚𝐢𝐧𝐭 𝐛𝐲 𝐈𝐧𝐩𝐚𝐢𝐧𝐭 - Train model to add objects into images by first learning how to remove them. 🎨 Simplifies complex image editing. 🤩 The results are really impressive! Details & links👇

🎉 Exciting news! Our paper has been accepted to #ECCV2024 European Conference on Computer Vision #ECCV2026! Check out our project page for more details, including our results on the DTU dataset! Special thanks to MrNeRF and Zhenjun Zhao for sharing our work! 🙏

🎉 Excited to share that Class-Conditioned Transformation for Enhanced Robust Image Classification, led by Tsachi Blau, has been accepted to #WACV25! 🚀 CODIP boosts the robustness of trained models by leveraging the perceptually aligned gradients property, without training!

🎉 Two papers accepted to #CVPR2025! 🖌 Paint by Inpaint: Learning to Add Image Objects by Removing Them First! 🔗 [rotsteinnoam.github.io/Paint-by-Inpai…] 📽 Pathways on the Image Manifold: Image Editing via Video Generation! 🔗 [rotsteinnoam.github.io/Frame2Frame/] Check out their project pages! 🚀

Excited to share that our paper, "DocVLM: Make Your VLM an Efficient Reader," got accepted to CVPR! 🎉 Unlike general vision tasks, document understanding with Vision-Language Models demands high-resolution images, leading to a significant computational burden. #CVPR2025 #AI #LLM