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, Daniel Wang, Fengyu Yang, Hyoungseob Park, 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 • 21 Mar 2024 • Blake Gella, Howard Zhang, Rishi Upadhyay, Tiffany Chang, Nathan Wei, Matthew Waliman, Yunhao Ba, Celso de Melo, Alex Wong, Achuta Kadambi
We propose a method to infer semantic segmentation maps from images captured under adverse weather conditions.
no code implementations • 18 Mar 2024 • Howard Zhang, Yunhao Ba, Ethan Yang, Rishi Upadhyay, Alex Wong, Achuta Kadambi, Yun Guo, Xueyao Xiao, Xiaoxiong Wang, Yi Li, Yi Chang, Luxin Yan, Chaochao Zheng, Luping Wang, Bin Liu, Sunder Ali Khowaja, Jiseok Yoon, Ik-Hyun Lee, Zhao Zhang, Yanyan Wei, Jiahuan Ren, Suiyi Zhao, Huan Zheng
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
no code implementations • 22 Feb 2024 • Xu Shen, Yongkeun Choi, Alex Wong, Francesco Borrelli, Scott Moura, Soomin Woo
This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs).
no code implementations • 5 Feb 2024 • Hyoungseob Park, Anjali Gupta, Alex Wong
During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i. e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain.
no code implementations • 31 Jan 2024 • Fengyu Yang, Chao Feng, Ziyang Chen, Hyoungseob Park, Daniel Wang, Yiming Dou, Ziyao Zeng, Xien Chen, Rit Gangopadhyay, Andrew Owens, Alex Wong
We introduce UniTouch, a unified tactile model for vision-based touch sensors connected to multiple modalities, including vision, language, and sound.
no code implementations • 15 Dec 2023 • Blake Gella, Howard Zhang, Rishi Upadhyay, Tiffany Chang, Matthew Waliman, Yunhao Ba, Alex Wong, Achuta Kadambi
To this end, we create the WeatherProof Dataset, the first semantic segmentation dataset with accurate clear and adverse weather image pairs, which not only enables our new training paradigm, but also improves the evaluation of the performance gap between clear and degraded segmentation.
no code implementations • 1 Dec 2023 • Xiaoran Zhang, Daniel H. Pak, Shawn S. Ahn, Xiaoxiao Li, Chenyu You, Lawrence Staib, Albert J. Sinusas, Alex Wong, James S. Duncan
This paper proposes a heteroscedastic uncertainty estimation framework for unsupervised medical image registration.
no code implementations • 1 Dec 2023 • Xiaoran Zhang, John C. Stendahl, Lawrence Staib, Albert J. Sinusas, Alex Wong, James S. Duncan
As the unsupervised learning scheme relies on intensity constancy to establish correspondence between images for reconstruction, this introduces spurious error residuals that are not modeled by the typical training objective.
no code implementations • 1 Dec 2023 • Rishi Upadhyay, Howard Zhang, Yunhao Ba, Ethan Yang, Blake Gella, Sicheng Jiang, Alex Wong, Achuta Kadambi
We show that outputs of models trained with this constraint both appear more realistic and improve performance of downstream models trained on generated images.
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 • 16 Sep 2023 • Marvin Chancán, Alex Wong, Ian Abraham
Training with data collected by our approach improves depth completion by an average greater than 18% across four depth completion models compared to existing exploration methods on the MP3D test set.
no code implementations • CVPR 2023 • Howard Zhang, Yunhao Ba, Ethan Yang, Varan Mehra, Blake Gella, Akira Suzuki, Arnold Pfahnl, Chethan Chinder Chandrappa, Alex Wong, Achuta Kadambi
We introduce a pipeline that uses the power of light-transport physics and a model trained on a small, initial seed dataset to reject approximately 99. 6% of unwanted scenes.
no code implementations • CVPR 2023 • Akash Deep Singh, Yunhao Ba, Ankur Sarker, Howard Zhang, Achuta Kadambi, Stefano Soatto, Mani Srivastava, Alex Wong
To fuse radar depth with an image, we propose a gated fusion scheme that accounts for the confidence scores of the correspondence so that we selectively combine radar and camera embeddings to yield a dense depth map.
1 code implementation • 14 Nov 2022 • Alexandre Tiard, Alex Wong, David Joon Ho, Yangchao Wu, Eliram Nof, Alvin C. Goh, Stefano Soatto, Saad Nadeem
Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets ranging from tumor classification (CAMELYON17) and subtyping (BRACS) to HER2 status classification and treatment response prediction.
1 code implementation • 22 Jun 2022 • Yunhao Ba, Howard Zhang, Ethan Yang, Akira Suzuki, Arnold Pfahnl, Chethan Chinder Chandrappa, Celso de Melo, Suya You, Stefano Soatto, Alex Wong, Achuta Kadambi
We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image.
1 code implementation • 30 Mar 2022 • Tian Yu Liu, Parth Agrawal, Allison Chen, Byung-Woo Hong, Alex Wong
In the absence of ground truth for model selection and training, our method, termed Monitored Distillation, allows a student to exploit a blind ensemble of teachers by selectively learning from predictions that best minimize the reconstruction error for a given image.
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.
1 code implementation • CVPR 2022 • Zachary Berger, Parth Agrawal, Tian Yu Liu, Stefano Soatto, Alex Wong
We study the effect of adversarial perturbations of images on deep stereo matching networks for the disparity estimation task.
1 code implementation • 18 Sep 2021 • Alex Wong, Allison Chen, Yangchao Wu, Safa Cicek, Alexandre Tiard, Byung-Woo Hong, Stefano Soatto
We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network.
1 code implementation • ICCV 2021 • Alex Wong, Stefano Soatto
At inference time, the calibration of the camera, which can be different than the one used for training, is fed as an input to the network along with the sparse point cloud and a single image.
Ranked #2 on Depth Completion on VOID
1 code implementation • 6 Jun 2021 • Alex Wong, Xiaohan Fei, Byung-Woo Hong, Stefano Soatto
We present a method to infer a dense depth map from a color image and associated sparse depth measurements.
1 code implementation • 6 Jun 2021 • Alex Wong, Safa Cicek, Stefano Soatto
We present a method for inferring dense depth maps from images and sparse depth measurements by leveraging synthetic data to learn the association of sparse point clouds with dense natural shapes, and using the image as evidence to validate the predicted depth map.
Ranked #3 on Depth Completion on VOID
1 code implementation • 21 Sep 2020 • Alex Wong, Mukund Mundhra, Stefano Soatto
We study the effect of adversarial perturbations of images on the estimates of disparity by deep learning models trained for stereo.
1 code implementation • NeurIPS 2020 • Alex Wong, Safa Cicek, Stefano Soatto
We study the effect of adversarial perturbations on the task of monocular depth prediction.
2 code implementations • 15 May 2019 • Alex Wong, Xiaohan Fei, Stephanie Tsuei, Stefano Soatto
Our method first constructs a piecewise planar scaffolding of the scene, and then uses it to infer dense depth using the image along with the sparse points.
Ranked #4 on Depth Completion on VOID
1 code implementation • CVPR 2019 • Alex Wong, Byung-Woo Hong, Stefano Soatto
Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth.
no code implementations • CVPR 2019 • Yanchao Yang, Alex Wong, Stefano Soatto
We present a deep learning system to infer the posterior distribution of a dense depth map associated with an image, by exploiting sparse range measurements, for instance from a lidar.
Ranked #5 on Depth Completion on VOID
2 code implementations • 30 Jul 2018 • Xiaohan Fei, Alex Wong, Stefano Soatto
We propose using global orientation from inertial measurements, and the bias it induces on the shape of objects populating the scene, to inform visual 3D reconstruction.
no code implementations • ICCV 2015 • Alex Wong, Alan L. Yuille
The task of discriminating one object from another is almost trivial for a human being.
no code implementations • 21 Nov 2015 • Xuan Dong, Yu Zhu, Weixin Li, Lingxi Xie, Alex Wong, Alan Yuille
In this paper, we proposed to use both fidelity (the difference with original images) and naturalness (human visual perception of super resolved images) for evaluation.