Search Results for author:
Found 17 papers, 6 papers with code
Scalable Pre-training of Large Autoregressive Image Models
Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks.
Ranked #336 on
Image Classification
on ImageNet
(using extra training data)
Generating Molecular Conformer Fields
In this paper we tackle the problem of generating conformers of a molecule in 3D space given its molecular graph.
Adaptivity and Modularity for Efficient Generalization Over Task Complexity
We investigate how the use of a mechanism for adaptive and modular computation in transformers facilitates the learning of tasks that demand generalization over the number of sequential computation steps (i. e., the depth of the computation graph).
Pseudo-Generalized Dynamic View Synthesis from a Video
In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.
Value function estimation using conditional diffusion models for control
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data.
Manifold Diffusion Fields
Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold.
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation
In this work, we propose f-DM, a generalized family of DMs which allows progressive signal transformation.
GAUDI: A Neural Architect for Immersive 3D Scene Generation
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.
Ranked #1 on
Image Generation
on ARKitScenes
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.
Fast and Explicit Neural View Synthesis
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.
Unconstrained Scene Generation with Locally Conditioned Radiance Fields
In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera.
Ranked #1 on
Scene Generation
on VizDoom
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.
On the generalization of learning-based 3D reconstruction
State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training.
Set Distribution Networks: a Generative Model for Sets of Images
We introduce Set Distribution Networks (SDNs), a novel framework that learns to autoencode and freely generate sets.
Equivariant Neural Rendering
We propose a framework for learning neural scene representations directly from images, without 3D supervision.
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric.
Error-Correcting Factorization
To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes.
