Search Results for author: Shufan Li

Found 8 papers, 6 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.

Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data

1 code implementation8 Feb 2024 Shufan Li, Harkanwar Singh, Aditya Grover

A recent architecture, Mamba, based on state space models has been shown to achieve comparable performance for modeling text sequences, while scaling linearly with the sequence length.

Action Recognition Weather Forecasting

InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction Following

1 code implementation11 Dec 2023 Shufan Li, Harkanwar Singh, Aditya Grover

We demonstrate that our system can perform a series of novel instruction-guided editing tasks.

Instruction Following

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

no code implementations 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

Refine and Represent: Region-to-Object Representation Learning

1 code implementation25 Aug 2022 Akash Gokul, Konstantinos Kallidromitis, Shufan Li, Yusuke Kato, Kazuki Kozuka, Trevor Darrell, Colorado J Reed

Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives.

Object Representation Learning +4

Interpreting Audiograms with Multi-stage Neural Networks

1 code implementation17 Dec 2021 Shufan Li, Congxi Lu, Linkai Li, Jirong Duan, Xinping Fu, Haoshuai Zhou

Audiograms are a particular type of line charts representing individuals' hearing level at various frequencies.

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