Search Results for author:
Found 14 papers, 4 papers with code
DOCCI: Descriptions of Connected and Contrasting Images
Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research.
Prompt Expansion for Adaptive Text-to-Image Generation
Text-to-image generation models are powerful but difficult to use.
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning
Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions.
Ranked #1 on
Vision and Language Navigation
on RxR
(using extra training data)
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge.
Ranked #1 on
Text-to-Image Generation
on LAION COCO
Vector-quantized Image Modeling with Improved VQGAN
Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively.
PanGEA: The Panoramic Graph Environment Annotation Toolkit
PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments.
On the Evaluation of Vision-and-Language Navigation Instructions
Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions.
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding
We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset.
Ranked #5 on
Vision and Language Navigation
on RxR
Transferable Representation Learning in Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals.
Ranked #115 on
Vision and Language Navigation
on VLN Challenge
General Evaluation for Instruction Conditioned Navigation using Dynamic Time Warping
We address fundamental flaws in previously used metrics and show how Dynamic Time Warping (DTW), a long known method of measuring similarity between two time series, can be used for evaluation of navigation agents.
Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths.
Image Transformer
Image generation has been successfully cast as an autoregressive sequence generation or transformation problem.
Ranked #3 on
Density Estimation
on CIFAR-10
Neural Networks for irregularly observed continuous-time Stochastic Processes
Designing neural networks for continuous-time stochastic processes is challenging, especially when observations are made irregularly.
Capturing Human Category Representations by Sampling in Deep Feature Spaces
In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners.
