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Found 9 papers, 5 papers with code
Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression
Evaluation results show our method improves visual quality by 14%, prompt alignment by 16. 2% and scene diversity by 15. 3%, compared to prompt engineering the base Emu model for stickers generation.
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.
Revisiting Hotels-50K and Hotel-ID
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID.
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
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.
Reinforced active learning for image segmentation
Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems.
On the iterative refinement of densely connected representation levels for semantic segmentation
State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Ranked #1 on
Node Classification
on Pubmed
(Validation metric)
