Search Results for author: Mihir Prabhudesai

Found 7 papers, 5 papers with code

Your Diffusion Model is Secretly a Zero-Shot Classifier

1 code implementation28 Mar 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

Test-time Adaptation with Slot-Centric Models

1 code implementation21 Mar 2022 Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki

In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases.

Image Classification Image Segmentation +6

CoCoNets: Continuous Contrastive 3D Scene Representations

1 code implementation CVPR 2021 Shamit Lal, Mihir Prabhudesai, Ishita Mediratta, Adam W. Harley, Katerina Fragkiadaki

This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection.

3D Object Detection Contrastive Learning +3

3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators

no code implementations12 Nov 2020 Hsiao-Yu Fish Tung, Zhou Xian, Mihir Prabhudesai, Shamit Lal, Katerina Fragkiadaki

Object motion predictions are computed by a graph neural network that operates over the object features extracted from the 3D neural scene representation.

Disentangling 3D Prototypical Networks For Few-Shot Concept Learning

1 code implementation ICLR 2021 Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam W Harley, Katerina Fragkiadaki

We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D object detection and few-shot concept classification.

3D Object Detection object-detection +2

3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations

no code implementations30 Oct 2020 Mihir Prabhudesai, Shamit Lal, Hsiao-Yu Fish Tung, Adam W. Harley, Shubhankar Potdar, Katerina Fragkiadaki

We can compare the 3D feature maps of two objects by searching alignment across scales and 3D rotations, and, as a result of the operation, we can estimate pose and scale changes without the need for 3D pose annotations.

3D Object Recognition Pose Estimation +1

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