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Found 10 papers, 7 papers with code
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt.
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements.
PIXART-δ: Fast and Controllable Image Generation with Latent Consistency Models
As a state-of-the-art, open-source image generation model, PIXART-{\delta} offers a promising alternative to the Stable Diffusion family of models, contributing significantly to text-to-image synthesis.
Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss
In this work, we introduce two scaled-down variants, Segmind Stable Diffusion (SSD-1B) and Segmind-Vega, with 1. 3B and 0. 74B parameter UNets, respectively, achieved through progressive removal using layer-level losses focusing on reducing the model size while preserving generative quality.
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning
Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies.
Vision Transformers are Robust Learners
Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling
In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch.
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling
In this work, we propose that, with the normalized temperature-scaled cross-entropy (NT-Xent) loss function (as used in SimCLR), it is beneficial to not have images of the same category in the same batch.
