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Found 16 papers, 7 papers with code
Object-centric Binding in Contrastive Language-Image Pretraining
However, these models have limitations in understanding complex compositional scenes involving multiple objects and their spatial relationships.
EvalGIM: A Library for Evaluating Generative Image Models
To enable actionable evaluation insights, we introduce ''Evaluation Exercises'' that highlight takeaways for specific evaluation questions.
Boosting Latent Diffusion with Perceptual Objectives
LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder.
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
In this work, we perform an in-depth study of LDM training recipes focusing on the performance of models and their training efficiency.
$\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Based on this observation, we revise the standard contrastive loss to explicitly encode how a sample relates to others.
Consistency-diversity-realism Pareto fronts of conditional image generative models
Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as world simulators.
An Introduction to Vision-Language Modeling
Then, we present and discuss approaches to evaluate VLMs.
Improving Text-to-Image Consistency via Automatic Prompt Optimization
In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models.
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be undesirable.
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Curation methods for massive vision-language datasets trade off between dataset size and quality.
Semi-supervised learning made simple with self-supervised clustering
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations.
Improved baselines for vision-language pre-training
Indeed, we find that a simple CLIP baseline can also be improved substantially, up to a 25% relative improvement on downstream zero-shot tasks, by using well-known training techniques that are popular in other subfields.
Instance-Conditioned GAN Data Augmentation for Representation Learning
We showcase the benefits of DA_IC-GAN by plugging it out-of-the-box into the supervised training of ResNets and DeiT models on the ImageNet dataset, and achieving accuracy boosts up to between 1%p and 2%p with the highest capacity models.
Supervised Tractogram Filtering using Geometric Deep Learning
A tractogram is a virtual representation of the brain white matter.
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
The intuitive idea is to model a fiber as a point cloud and the goal is to investigate whether and how a geometric deep learning model might capture its anatomical properties.
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images.
