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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 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

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!

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