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Found 18 papers, 11 papers with code
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.
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
Any prior knowledge can now be embedded into that metric space independently from the employed DA.
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.
Feedback-guided Data Synthesis for Imbalanced Classification
In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model.
Graph Inductive Biases in Transformers without Message Passing
Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings.
Ranked #1 on
Node Classification
on PATTERN
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.
Controllable Image Generation via Collage Representations
Our experiments on the OI dataset show that M&Ms outperforms baselines in terms of fine-grained scene controllability while being very competitive in terms of image quality and sample diversity.
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.
Learning to Substitute Ingredients in Recipes
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary exploration in everyone's kitchen.
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects
Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem.
Uncertainty-Driven Active Vision for Implicit Scene Reconstruction
We evaluate our proposed approach on the ABC dataset and the in the wild CO3D dataset, and show that: (1) we are able to obtain high quality state-of-the-art occupancy reconstructions; (2) our perspective conditioned uncertainty definition is effective to drive improvements in next best view selection and outperforms strong baseline approaches; and (3) we can further improve shape understanding by performing a gradient-based search on the view selection candidates.
Revisiting Hotels-50K and Hotel-ID
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID.
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction
Most current approaches to undersampled multi-coil MRI reconstruction focus on learning the reconstruction model for a fixed, equidistant acquisition trajectory.
Parameter Prediction for Unseen Deep Architectures
We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet.
Ranked #1 on
Parameter Prediction
on CIFAR10
Instance-Conditioned GAN
Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces.
Ranked #1 on
Conditional Image Generation
on ImageNet 64x64
Graph Attention Networks with Positional Embeddings
In this framework, the positional embeddings are learned by a model predictive of the graph context, plugged into an enhanced GAT architecture, which is able to leverage both the positional and content information of each node.
Generating unseen complex scenes: are we there yet?
In this paper, we propose a methodology to compare complex scene conditional generation models, and provide an in-depth analysis that assesses the ability of each model to (1) fit the training distribution and hence perform well on seen conditionings, (2) to generalize to unseen conditionings composed of seen object combinations, and (3) generalize to unseen conditionings composed of unseen object combinations.
