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Found 6 papers, 1 papers with code
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs
For model evaluation, we establish a comprehensive benchmark encompassing all the aforementioned tasks.
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #18 on
Visual Question Answering
on MM-Vet
Controllable Music Production with Diffusion Models and Guidance Gradients
We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44. 1kHz stereo audio with sampling-time guidance.
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
We empirically show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off between performance and efficiency than the corresponding dense ViTs.
Masked Autoencoding Does Not Help Natural Language Supervision at Scale
Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks.
Bias in Automated Image Colorization: Metrics and Error Types
We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model.
