Search Results for author: Shivam Duggal

Found 7 papers, 3 papers with code

A Vision Check-up for Language Models

no code implementations3 Jan 2024 Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad, Stephanie Fu, Adrian Rodriguez-Munoz, Shivam Duggal, Phillip Isola, Antonio Torralba

Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world.

Image Generation Representation Learning

Your Diffusion Model is Secretly a Zero-Shot Classifier

2 code implementations ICCV 2023 Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak

Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.

Domain Generalization Fine-Grained Image Classification +5

NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation

no code implementations12 Jun 2022 Trevor Houchens, Cheng-You Lu, Shivam Duggal, Rao Fu, Srinath Sridhar

We propose Omnidirectional Distance Fields (ODFs), a new 3D shape representation that encodes geometry by storing the depth to the object's surface from any 3D position in any viewing direction.

3D Shape Representation

Topologically-Aware Deformation Fields for Single-View 3D Reconstruction

1 code implementation CVPR 2022 Shivam Duggal, Deepak Pathak

The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections and their poses without any 3D supervision.

3D Reconstruction Object +1

Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild

no code implementations18 Jan 2021 Shivam Duggal, ZiHao Wang, Wei-Chiu Ma, Sivabalan Manivasagam, Justin Liang, Shenlong Wang, Raquel Urtasun

Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.

3D Object Reconstruction

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