Search Results for author: Alexandros Graikos

Found 9 papers, 6 papers with code

Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following

no code implementations4 Jun 2024 Qiaomu Miao, Alexandros Graikos, Jingwei Zhang, Sounak Mondal, Minh Hoai, Dimitris Samaras

We obtain the first prior using a large pretrained Visual Question Answering (VQA) model, where we compute Grad-CAM heatmaps by `prompting' the VQA model with a gaze following question.

Question Answering Visual Question Answering

Conditional Generation from Unconditional Diffusion Models using Denoiser Representations

1 code implementation2 Jun 2023 Alexandros Graikos, Srikar Yellapragada, Dimitris Samaras

Our approach provides a powerful and flexible way to adapt diffusion models to new conditions and generate high-quality augmented data for various conditional generation tasks.

Attribute Data Augmentation +1

GFlowNet-EM for learning compositional latent variable models

1 code implementation13 Feb 2023 Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie Everett, Alexandros Graikos, Yoshua Bengio

Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large number of possible configurations of the latents.

Variational Inference

Diffusion models as plug-and-play priors

1 code implementation17 Jun 2022 Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, Dimitris Samaras

We consider the problem of inferring high-dimensional data $\mathbf{x}$ in a model that consists of a prior $p(\mathbf{x})$ and an auxiliary differentiable constraint $c(\mathbf{x},\mathbf{y})$ on $x$ given some additional information $\mathbf{y}$.

Combinatorial Optimization Denoising +2

Resolving label uncertainty with implicit posterior models

1 code implementation28 Feb 2022 Esther Rolf, Nikolay Malkin, Alexandros Graikos, Ana Jojic, Caleb Robinson, Nebojsa Jojic

We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label.

Common Sense Reasoning Segmentation +2

Resolving label uncertainty with implicit generative models

no code implementations29 Sep 2021 Esther Rolf, Nikolay Malkin, Alexandros Graikos, Ana Jojic, Caleb Robinson, Nebojsa Jojic

In prediction problems, coarse and imprecise sources of input can provide rich information about labels, but are not readily used by discriminative learners.

Common Sense Reasoning Segmentation +2

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