115 citations · 138 across the 7 of their papers we have counts for
9 papers · 1 filter
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models
Yuan-Hong Liao, Rafid Mahmood, Sanja Fidler +1
Despite recent advances demonstrating vision-language models' (VLMs) abilities to describe complex relationships in images using natural language, their capability to quantitativel…
Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data
Xi Yan, David Acuna, Sanja Fidler
Transfer learning has proven to be a successful technique to train deep learning models in the domains where little training data is available. The dominant approach is to pretrain…
Neural Turtle Graphics for Modeling City Road Layouts
Hang Chu, Daiqing Li, David Acuna +6
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the…
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Towaki Takikawa, David Acuna, Varun Jampani +1
Current state-of-the-art methods for image segmentation form a dense image representation where the color, shape and texture information are all processed together inside a deep CN…
Meta-Sim: Learning to Generate Synthetic Datasets
Amlan Kar, Aayush Prakash, Ming-Yu Liu +6
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get. The goal of our work is to automatically synthesize labeled dat…
Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations
David Acuna, Amlan Kar, Sanja Fidler
We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a signific…