1 code implementation • 17 Apr 2024 • Sherry X. Chen, Yaron Vaxman, Elad Ben Baruch, David Asulin, Aviad Moreshet, Kuo-Chin Lien, Misha Sra, Pradeep Sen
Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e. g., with a specific object or person), or on optimizing the weights, text prompts, and/or learning features for each input image in an attempt to coax the image generator to produce the desired result.
no code implementations • 8 Nov 2022 • Steve Bako, Pradeep Sen, Anton Kaplanyan
The goal of an ideal level of detail (LoD) method is to make rendering costs independent of the 3D scene complexity, while preserving the appearance of the scene.
1 code implementation • CVPR 2022 • Chengyuan Xu, Boning Dong, Noah Stier, Curtis McCully, D. Andrew Howell, Pradeep Sen, Tobias Höllerer
We introduce an interactive image segmentation and visualization framework for identifying, inspecting, and editing tiny objects (just a few pixels wide) in large multi-megapixel high-dynamic-range (HDR) images.
1 code implementation • 1 Dec 2021 • Noah Stier, Alexander Rich, Pradeep Sen, Tobias Höllerer
To this end, we introduce VoRTX, an end-to-end volumetric 3D reconstruction network using transformers for wide-baseline, multi-view feature fusion.
1 code implementation • 1 Dec 2021 • Alexander Rich, Noah Stier, Pradeep Sen, Tobias Höllerer
Furthermore, unlike existing volumetric MVS techniques, our 3D CNN operates on a feature-augmented point cloud, allowing for effective aggregation of multi-view information and flexible iterative refinement of depth maps.
Ranked #5 on 3D Action Recognition on NTU RGB+D
no code implementations • 23 Nov 2021 • Yi Ding, Alex Rich, Mason Wang, Noah Stier, Matthew Turk, Pradeep Sen, Tobias Höllerer
Multimodal classification is a core task in human-centric machine learning.
2 code implementations • 28 Jun 2021 • Chengyuan Xu, Curtis McCully, Boning Dong, D. Andrew Howell, Pradeep Sen
2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR detection models.
1 code implementation • 16 Apr 2021 • Ekta Prashnani, Orazio Gallo, Joohwan Kim, Josef Spjut, Pradeep Sen, Iuri Frosio
We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observers varies with the frame, as it depends on the content of the scene.
1 code implementation • CVPR 2021 • Abhishek Badki, Orazio Gallo, Jan Kautz, Pradeep Sen
Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene -- even for humans.
1 code implementation • CVPR 2020 • Abhishek Badki, Alejandro Troccoli, Kihwan Kim, Jan Kautz, Pradeep Sen, Orazio Gallo
Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels.
1 code implementation • CVPR 2020 • Abhishek Badki, Orazio Gallo, Jan Kautz, Pradeep Sen
Meshlets act as a dictionary of local features and thus allow to use learned priors to reconstruct object meshes in any pose and from unseen classes, even when the noise is large and the samples sparse.
1 code implementation • CVPR 2018 • Ekta Prashnani, Hong Cai, Yasamin Mostofi, Pradeep Sen
Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels.
Ranked #1 on Video Quality Assessment on MSU SR-QA Dataset
no code implementations • 3 Jun 2018 • Chieh-Chi Kao, Yu-Xiang Wang, Jonathan Waltman, Pradeep Sen
Image hallucination and super-resolution have been studied for decades, and many approaches have been proposed to upsample low-resolution images using information from the images themselves, multiple example images, or large image databases.
2 code implementations • 28 May 2018 • Shayan Sadigh, Pradeep Sen
We describe a new class of subsampling techniques for CNNs, termed multisampling, that significantly increases the amount of information kept by feature maps through subsampling layers.
no code implementations • 16 Jan 2018 • Chieh-Chi Kao, Teng-Yok Lee, Pradeep Sen, Ming-Yu Liu
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification.
no code implementations • 4 Oct 2017 • Qiaodong Cui, Victor Fragoso, Chris Sweeney, Pradeep Sen
We present GraphMatch, an approximate yet efficient method for building the matching graph for large-scale structure-from-motion (SfM) pipelines.
no code implementations • 27 Sep 2017 • Victor Fragoso, Chris Sweeney, Pradeep Sen, Matthew Turk
While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with positional error (noise).
no code implementations • ACM Transactions on Graphics 2017 • Steve Bako, Thijs Vogels, Brian McWilliams, Mark Meyer, Jan Novák, Alex Harvill, Pradeep Sen, Tony Derose, Fabrice Rousselle
In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors.
2 code implementations • ACCV 2017 • Steve Bako, Soheil Darabi, Eli Shechtman, Jue Wang, Kalyan Sunkavalli, Pradeep Sen
In this work, we automatically detect and remove distracting shadows from photographs of documents and other text-based items.