no code implementations • 6 May 2024 • Dong Lao, Congli Wang, Alex Wong, Stefano Soatto
Rather than initializing a latent irradiance ("template") by heuristics to estimate deformation, we select one of the images as a reference, and model the deformation in this image by the aggregation of the optical flow from it to other images, exploiting a prior imposed by Central Limit Theorem.
1 code implementation • 4 Apr 2024 • Ziyao Zeng, Hyoungseob Park, Daniel Wang, Fengyu Yang, Yangchao Wu, Stefano Soatto, Byung-Woo Hong, Dong Lao, Alex Wong
To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene.
no code implementations • 15 Oct 2023 • Yangchao Wu, Tian Yu Liu, Hyoungseob Park, Stefano Soatto, Dong Lao, Alex Wong
The sparse depth modality have seen even less as intensity transformations alter the scale of the 3D scene, and geometric transformations may decimate the sparse points during resampling.
1 code implementation • 6 Oct 2023 • Dong Lao, Yangchao Wu, Tian Yu Liu, Alex Wong, Stefano Soatto
Vision Transformer (ViT) architectures represent images as collections of high-dimensional vectorized tokens, each corresponding to a rectangular non-overlapping patch.
no code implementations • 4 Apr 2023 • Dong Lao, Zhengyang Hu, Francesco Locatello, Yanchao Yang, Stefano Soatto
We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision.
no code implementations • 4 Jun 2022 • Yuxin Sun, Dong Lao, Ganesh Sundaramoorthi, Anthony Yezzi
We discover restrained numerical instabilities in current training practices of deep networks with stochastic gradient descent (SGD).
no code implementations • 26 Mar 2022 • Dong Lao, Alex Wong, Samuel Lu, Stefano Soatto
We explore how pre-training a model to infer depth from a single image compares to pre-training the model for a semantic task, e. g. ImageNet classification, for the purpose of downstream transfer to semantic segmentation.
no code implementations • NeurIPS Workshop DLDE 2021 • Yuxin Sun, Dong Lao, Ganesh Sundaramoorthi, Anthony Yezzi
We introduce a recently developed framework PDE Acceleration, which is a variational approach to accelerated optimization with partial differential equations (PDE), in the context of optimization of deep networks.
no code implementations • ICCV 2021 • Dong Lao, Peihao Zhu, Peter Wonka, Ganesh Sundaramoorthi
We consider the problem of filling in missing spatio-temporal regions of a video.
no code implementations • 25 Aug 2020 • Dong Lao, Peihao Zhu, Peter Wonka, Ganesh Sundaramoorthi
We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error.
1 code implementation • CVPR 2020 • Yanchao Yang, Dong Lao, Ganesh Sundaramoorthi, Stefano Soatto
We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a different domain, a problem known as unsupervised domain adaptation.
1 code implementation • ICCV 2019 • Dong Lao, Ganesh Sundaramoorthi
We consider the problem of detecting objects, as they come into view, from videos in an online fashion.
1 code implementation • ECCV 2018 • Dong Lao, Ganesh Sundaramoorthi
We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera.
no code implementations • CVPR 2017 • Dong Lao, Ganesh Sundaramoorthi
Our method is designed to detect the object(s) with minimum delay, i. e., frames after the object moves, constraining the false alarms.
no code implementations • 24 May 2016 • Dong Lao, Ganesh Sundaramoorthi
We present a general framework and method for simultaneous detection and segmentation of an object in a video that moves (or comes into view of the camera) at some unknown time in the video.