152 citations · 182 across the 9 of their papers we have counts for
9 papers
Navigating Hallucinations for Reasoning of Unintentional Activities
Shresth Grover, Vibhav Vineet, Yogesh S Rawat
In this work we present a novel task of understanding unintentional human activities in videos. We formalize this problem as a reasoning task under zero-shot scenario, where given…
DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
Yash Jain, Harkirat Behl, Zsolt Kira +1
Construction of a universal detector poses a crucial question: How can we most effectively train a model on a large mixture of datasets? The answer lies in learning dataset-specifi…
Efficiently Robustify Pre-trained Models
Nishant Jain, Harkirat Behl, Yogesh Singh Rawat +1
A recent trend in deep learning algorithms has been towards training large scale models, having high parameter count and trained on big dataset. However, robustness of such large s…
Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao +3
We propose a new paradigm to automatically generate training data with accurate labels at scale using the text-to-image synthesis frameworks (e.g., DALL-E, Stable Diffusion, etc.).…
Controllable Text-to-Image Generation with GPT-4
Tianjun Zhang, Yi Zhang, Vibhav Vineet +2
Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LL…
Neural-Sim: Learning to Generate Training Data with NeRF
Yunhao Ge, Harkirat Behl, Jiashu Xu +6
Training computer vision models usually requires collecting and labeling vast amounts of imagery under a diverse set of scene configurations and properties. This process is incredi…