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Found 10 papers, 3 papers with code
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
We present MM1. 5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning.
Ranked #55 on
Visual Question Answering
on MM-Vet
CoLay: Controllable Layout Generation through Multi-conditional Latent Diffusion
Layout design generation has recently gained significant attention due to its potential applications in various fields, including UI, graphic, and floor plan design.
Leveraging Human Revisions for Improving Text-to-Layout Models
Our method, Revision-Aware Reward Models ($\method$), allows a generative text-to-layout model to produce more modern, designer-aligned layouts, showing the potential for utilizing human revisions and stronger forms of feedback in improving generative models.
Automatic Macro Mining from Interaction Traces at Scale
Macros are building block tasks of our everyday smartphone activity (e. g., "login", or "booking a flight").
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
Layout design is an important task in various design fields, including user interface, document, and graphic design.
Sketch-based Creativity Support Tools using Deep Learning
Sketching is a natural and effective visual communication medium commonly used in creative processes.
Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models
The design process of user interfaces (UIs) often begins with articulating high-level design goals.
Scones: Towards Conversational Authoring of Sketches
Iteratively refining and critiquing sketches are crucial steps to developing effective designs.
Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications
Sketching and natural languages are effective communication media for interactive applications.
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.
