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Found 7 papers, 4 papers with code
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All
As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as "Virtual Try-All"-has become crucial.
Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Training multi-model models for histopathology requires instruction tuning datasets, which currently contain information for individual image patches, without a spatial grounding of the concepts within each patch and without a wider view of the WSI.
Quilt-1M: One Million Image-Text Pairs for Histopathology
From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of $802, 144$ image and text pairs.
DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling
We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image.
Multi-modal Masked Autoencoders Learn Compositional Histopathological Representations
However, the domain shift between natural images and digital pathology images requires further research in designing MAE for patch-level WSIs.
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling
Vision-and-Language Pre-training (VLP) improves model performance for downstream tasks that require image and text inputs.
Detecting Cybersecurity Events from Noisy Short Text
In this study, we propose a method that leverages both domain-specific word embeddings and task-specific features to detect cyber security events from tweets.
