Search Results for author: Ritwik Gupta

Found 13 papers, 7 papers with code

xT: Nested Tokenization for Larger Context in Large Images

1 code implementation4 Mar 2024 Ritwik Gupta, Shufan Li, Tyler Zhu, Jitendra Malik, Trevor Darrell, Karttikeya Mangalam

Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping.

See, Say, and Segment: Teaching LMMs to Overcome False Premises

no code implementations13 Dec 2023 Tsung-Han Wu, Giscard Biamby, David Chan, Lisa Dunlap, Ritwik Gupta, Xudong Wang, Joseph E. Gonzalez, Trevor Darrell

Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image.

Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning

1 code implementation ICCV 2023 Colorado J. Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, Trevor Darrell

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales.

Representation Learning

SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding

1 code implementation ICCV 2023 Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, Aniruddha Kembhavi

Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing.

Time Series Time Series Analysis

Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources

1 code implementation8 Aug 2022 Malachy Moran, Kayla Woputz, Derrick Hee, Manuela Girotto, Paolo D'Odorico, Ritwik Gupta, Daniel Feldman, Puya Vahabi, Alberto Todeschini, Colorado J Reed

Accurately estimating the snowpack in key mountainous basins is critical for water resource managers to make decisions that impact local and global economies, wildlife, and public policy.

WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data

no code implementations17 Sep 2021 Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta, Lucien Werner, Dava Newman

We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data.

Benchmarking BIG-bench Machine Learning +3

Region-level Active Detector Learning

no code implementations20 Aug 2021 Michael Laielli, Giscard Biamby, Dian Chen, Ritwik Gupta, Adam Loeffler, Phat Dat Nguyen, Ross Luo, Trevor Darrell, Sayna Ebrahimi

Active learning for object detection is conventionally achieved by applying techniques developed for classification in a way that aggregates individual detections into image-level selection criteria.

Active Learning Object +2

Proceedings of NeurIPS 2020 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response

no code implementations3 Dec 2020 Ritwik Gupta, Eric T. Heim, Edoardo Nemni

These are the "proceedings" of the 2nd AI + HADR workshop which was held virtually on December 12, 2020 as part of the Neural Information Processing Systems conference.

Disaster Response Humanitarian

Proceedings of NeurIPS 2019 Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response

no code implementations2 Dec 2020 Ritwik Gupta, Eric T. Heim

These are the "proceedings" of the 1st AI + HADR workshop which was held in Vancouver, Canada on December 13, 2019 as part of the Neural Information Processing Systems conference.

Disaster Response Humanitarian

xBD: A Dataset for Assessing Building Damage from Satellite Imagery

4 code implementations21 Nov 2019 Ritwik Gupta, Richard Hosfelt, Sandra Sajeev, Nirav Patel, Bryce Goodman, Jigar Doshi, Eric Heim, Howie Choset, Matthew Gaston

xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.

2D Semantic Segmentation Change Detection +2

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