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Found 11 papers, 6 papers with code
Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Model
In contrast, we advocate a generic, task-agnostic diffusion model, with several advancements such as log-scale depth parameterization to enable joint modeling of indoor and outdoor scenes, conditioning on the field-of-view (FOV) to handle scale ambiguity and synthetically augmenting FOV during training to generalize beyond the limited camera intrinsics in training datasets.
NeRFiller: Completing Scenes via Generative 3D Inpainting
In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text.
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity.
Monocular Depth Estimation using Diffusion Models
To cope with the limited availability of data for supervised training, we leverage pre-training on self-supervised image-to-image translation tasks.
Ranked #22 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
A Generalist Framework for Panoptic Segmentation of Images and Videos
Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image.
A Unified Sequence Interface for Vision Tasks
Despite that, by formulating the output of each task as a sequence of discrete tokens with a unified interface, we show that one can train a neural network with a single model architecture and loss function on all these tasks, with no task-specific customization.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.
Ranked #17 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Pix2seq: A Language Modeling Framework for Object Detection
We present Pix2Seq, a simple and generic framework for object detection.
Ranked #77 on
Object Detection
on COCO minival
(using extra training data)
Machine learning pipeline for battery state of health estimation
In this paper, we design and evaluate a machine learning pipeline for estimation of battery capacity fade - a metric of battery health - on 179 cells cycled under various conditions.
Non-Autoregressive Machine Translation with Latent Alignments
In addition, we adapt the Imputer model for non-autoregressive machine translation and demonstrate that Imputer with just 4 generation steps can match the performance of an autoregressive Transformer baseline.
Large-Scale Evolution of Image Classifiers
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
Ranked #117 on
Image Classification
on CIFAR-10
