no code implementations • 26 Feb 2024 • Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A. Efros, Aleksander Holynski
We introduce a method to generate 3D scenes that are disentangled into their component objects.
1 code implementation • 25 Jan 2024 • Letian Fu, Long Lian, Renhao Wang, Baifeng Shi, Xudong Wang, Adam Yala, Trevor Darrell, Alexei A. Efros, Ken Goldberg
In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE).
no code implementations • 19 Jan 2024 • Boyi Li, Jathushan Rajasegaran, Yossi Gandelsman, Alexei A. Efros, Jitendra Malik
This disentangled approach allows our method to generate a sequence of images that are faithful to the target motion in the 3D pose and, to the input image in terms of visual similarity.
no code implementations • 12 Dec 2023 • Yang Fu, Sifei Liu, Amey Kulkarni, Jan Kautz, Alexei A. Efros, Xiaolong Wang
While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis, it relies heavily on accurately pre-computed camera poses.
1 code implementation • 2 Nov 2023 • Assaf Shocher, Amil Dravid, Yossi Gandelsman, Inbar Mosseri, Michael Rubinstein, Alexei A. Efros
We define the target manifold as the set of all instances that $f$ maps to themselves.
1 code implementation • 9 Oct 2023 • Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt
We decompose the image representation as a sum across individual image patches, model layers, and attention heads, and use CLIP's text representation to interpret the summands.
no code implementations • 11 Jul 2023 • Renhao Wang, Yu Sun, Yossi Gandelsman, Xinlei Chen, Alexei A. Efros, Xiaolong Wang
Before making a prediction on each test instance, the model is trained on the same instance using a self-supervised task, such as image reconstruction with masked autoencoders.
no code implementations • NeurIPS 2023 • Tom Monnier, Jake Austin, Angjoo Kanazawa, Alexei A. Efros, Mathieu Aubry
We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio.
1 code implementation • ICCV 2023 • Sheng-Yu Wang, Alexei A. Efros, Jun-Yan Zhu, Richard Zhang
The problem of data attribution in such models -- which of the images in the training set are most responsible for the appearance of a given generated image -- is a difficult yet important one.
no code implementations • ICCV 2023 • Amil Dravid, Yossi Gandelsman, Alexei A. Efros, Assaf Shocher
In this paper, we demonstrate the existence of common features we call "Rosetta Neurons" across a range of models with different architectures, different tasks (generative and discriminative), and different types of supervision (class-supervised, text-supervised, self-supervised).
1 code implementation • NeurIPS 2023 • Dave Epstein, Allan Jabri, Ben Poole, Alexei A. Efros, Aleksander Holynski
However, many aspects of an image are difficult or impossible to convey through text.
2 code implementations • CVPR 2023 • George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu
Dataset Distillation aims to distill an entire dataset's knowledge into a few synthetic images.
1 code implementation • CVPR 2023 • Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu, Jimei Yang, Jingwan Lu, Alexei A. Efros, Krishna Kumar Singh
Given a scene image with a marked region and an image of a person, we insert the person into the scene while respecting the scene affordances.
1 code implementation • ICCV 2023 • Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa
We propose a method for editing NeRF scenes with text-instructions.
1 code implementation • 27 Feb 2023 • Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak
Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets.
6 code implementations • CVPR 2023 • Tim Brooks, Aleksander Holynski, Alexei A. Efros
We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
1 code implementation • 29 Sep 2022 • Alexander C. Li, Alexei A. Efros, Deepak Pathak
We empirically analyze these non-contrastive methods and find that SimSiam is extraordinarily sensitive to dataset and model size.
1 code implementation • 26 Sep 2022 • William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A. Efros, Jitendra Malik
We explore a data-driven approach for learning to optimize neural networks.
1 code implementation • 15 Sep 2022 • Yossi Gandelsman, Yu Sun, Xinlei Chen, Alexei A. Efros
Test-time training adapts to a new test distribution on the fly by optimizing a model for each test input using self-supervision.
no code implementations • 6 Sep 2022 • Vongani H. Maluleke, Neerja Thakkar, Tim Brooks, Ethan Weber, Trevor Darrell, Alexei A. Efros, Angjoo Kanazawa, Devin Guillory
In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets.
1 code implementation • 1 Sep 2022 • Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei A. Efros
How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification?
Ranked #5 on Personalized Segmentation on PerSeg
1 code implementation • 7 Jun 2022 • Tim Brooks, Janne Hellsten, Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming-Yu Liu, Alexei A. Efros, Tero Karras
Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dynamics and object persistence.
no code implementations • 5 May 2022 • Dave Epstein, Taesung Park, Richard Zhang, Eli Shechtman, Alexei A. Efros
Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network.
1 code implementation • 21 Apr 2022 • Tom Monnier, Matthew Fisher, Alexei A. Efros, Mathieu Aubry
Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry.
3D Object Reconstruction From A Single Image 3D Reconstruction +2
5 code implementations • CVPR 2022 • George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu
To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset.
no code implementations • CVPR 2022 • Zhangxing Bian, Allan Jabri, Alexei A. Efros, Andrew Owens
A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence.
no code implementations • 13 Dec 2021 • Tim Brooks, Alexei A. Efros
We double the capacity of our model with respect to StyleGAN2 to handle such complex data, and design a pose conditioning mechanism that drives our model to learn the nuanced relationship between pose and scene.
1 code implementation • CVPR 2022 • William Peebles, Jun-Yan Zhu, Richard Zhang, Antonio Torralba, Alexei A. Efros, Eli Shechtman
We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end.
1 code implementation • 29 Oct 2021 • Xi Shen, Alexei A. Efros, Armand Joulin, Mathieu Aubry
The goal of this work is to efficiently identify visually similar patterns in images, e. g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart.
no code implementations • ICCV 2021 • Zihang Lai, Sifei Liu, Alexei A. Efros, Xiaolong Wang
Relying on temporal continuity in videos, our work assumes that the 3D scene structure in nearby video frames remains static.
1 code implementation • NeurIPS 2021 • Dmitriy Smirnov, Michael Gharbi, Matthew Fisher, Vitor Guizilini, Alexei A. Efros, Justin Solomon
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters.
2 code implementations • CVPR 2021 • Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
Ranked #3 on 10-shot image generation on Babies
no code implementations • 6 Apr 2021 • Medhini Narasimhan, Shiry Ginosar, Andrew Owens, Alexei A. Efros, Trevor Darrell
We learn representations for video frames and frame-to-frame transition probabilities by fitting a video-specific model trained using contrastive learning.
1 code implementation • ICLR 2021 • Qiang Zhang, Tete Xiao, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang
We propose \textit{dynamics cycles} that align dynamic robot behavior across two domains using a cycle-consistency constraint.
no code implementations • ICLR 2021 • Tete Xiao, Xiaolong Wang, Alexei A. Efros, Trevor Darrell
Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations.
no code implementations • ECCV 2020 • Andrew Liu, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros, Noah Snavely
We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors.
10 code implementations • 30 Jul 2020 • Taesung Park, Alexei A. Efros, Richard Zhang, Jun-Yan Zhu
Furthermore, we draw negatives from within the input image itself, rather than from the rest of the dataset.
no code implementations • ECCV 2020 • Liqian Ma, Zhe Lin, Connelly Barnes, Alexei A. Efros, Jingwan Lu
Due to the ubiquity of smartphones, it is popular to take photos of one's self, or "selfies."
2 code implementations • ICLR 2021 • Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A. Efros, Lerrel Pinto, Xiaolong Wang
A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal.
4 code implementations • NeurIPS 2020 • Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang
Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging.
1 code implementation • NeurIPS 2020 • Allan Jabri, Andrew Owens, Alexei A. Efros
We cast correspondence as prediction of links in a space-time graph constructed from video.
2 code implementations • ECCV 2020 • Xi Shen, François Darmon, Alexei A. Efros, Mathieu Aubry
Coarse alignment is performed using RANSAC on off-the-shelf deep features.
4 code implementations • CVPR 2020 • Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros
In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.
3 code implementations • 29 Sep 2019 • Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt
In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.
Ranked #34 on Language Modelling on LAMBADA
Building change detection for remote sensing images CARLA MAP Leaderboard +6
3 code implementations • 26 Sep 2019 • Yu Sun, Eric Tzeng, Trevor Darrell, Alexei A. Efros
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.
no code implementations • 25 Sep 2019 • Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt
We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions.
1 code implementation • ICCV 2019 • Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman
We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects.
2 code implementations • ICCV 2019 • Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop.
no code implementations • ICLR 2019 • Kate Rakelly*, Evan Shelhamer*, Trevor Darrell, Alexei A. Efros, Sergey Levine
To explore generalization, we analyze guidance as a bridge between different levels of supervision to segment classes as the union of instances.
1 code implementation • CVPR 2019 • Xiaolong Wang, Allan Jabri, Alexei A. Efros
We introduce a self-supervised method for learning visual correspondence from unlabeled video.
1 code implementation • CVPR 2019 • Xi Shen, Alexei A. Efros, Mathieu Aubry
Our goal in this paper is to discover near duplicate patterns in large collections of artworks.
1 code implementation • NeurIPS 2019 • Deepak Pathak, Chris Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros
We evaluate the performance of these dynamic and modular agents in simulated environments.
5 code implementations • 27 Nov 2018 • Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros
Model distillation aims to distill the knowledge of a complex model into a simpler one.
no code implementations • ICLR 2019 • Dinesh Jayaraman, Frederik Ebert, Alexei A. Efros, Sergey Levine
Prediction is arguably one of the most basic functions of an intelligent system.
13 code implementations • ICCV 2019 • Caroline Chan, Shiry Ginosar, Tinghui Zhou, Alexei A. Efros
This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
2 code implementations • ECCV 2018 • Zhirong Wu, Alexei A. Efros, Stella X. Yu
Current major approaches to visual recognition follow an end-to-end formulation that classifies an input image into one of the pre-determined set of semantic categories.
4 code implementations • ICLR 2019 • Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros
However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the need for developing reward functions that are intrinsic to the agent.
Ranked #14 on Atari Games on Atari 2600 Montezuma's Revenge
1 code implementation • 25 May 2018 • Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine
Learning-based methods for visual segmentation have made progress on particular types of segmentation tasks, but are limited by the necessary supervision, the narrow definitions of fixed tasks, and the lack of control during inference for correcting errors.
3 code implementations • ECCV 2018 • Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros
In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.
1 code implementation • ICLR 2018 • Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
In our framework, the role of the expert is only to communicate the goals (i. e., what to imitate) during inference.
1 code implementation • ECCV 2018 • Andrew Owens, Alexei A. Efros
The thud of a bouncing ball, the onset of speech as lips open -- when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals.
no code implementations • 30 Mar 2018 • Jyh-Jing Hwang, Sergei Azernikov, Alexei A. Efros, Stella X. Yu
In the dental industry, it takes a technician years of training to design synthetic crowns that restore the function and integrity of missing teeth.
no code implementations • ECCV 2018 • Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, Jitendra Malik
The shape is represented as a deformable 3D mesh model of an object category where a shape is parameterized by a learned mean shape and per-instance predicted deformation.
1 code implementation • ICML 2018 • Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros
What makes humans so good at solving seemingly complex video games?
24 code implementations • CVPR 2018 • Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.
Ranked #19 on Video Quality Assessment on MSU FR VQA Database
no code implementations • CVPR 2018 • Shubham Tulsiani, Alexei A. Efros, Jitendra Malik
We present a framework for learning single-view shape and pose prediction without using direct supervision for either.
no code implementations • CVPR 2018 • David F. Fouhey, Wei-cheng Kuo, Alexei A. Efros, Jitendra Malik
A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data.
no code implementations • CVPR 2018 • Shubham Tulsiani, Saurabh Gupta, David Fouhey, Alexei A. Efros, Jitendra Malik
The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in terms of a small set of factors: a layout representing the enclosing surfaces as well as a set of objects represented in terms of shape and pose.
6 code implementations • NeurIPS 2017 • Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
Our proposed method encourages bijective consistency between the latent encoding and output modes.
3 code implementations • ICML 2018 • Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell
Domain adaptation is critical for success in new, unseen environments.
no code implementations • 26 Jul 2017 • Johanna Delanoy, Mathieu Aubry, Phillip Isola, Alexei A. Efros, Adrien Bousseau
The main strengths of our approach are its robustness to freehand bitmap drawings, its ability to adapt to different object categories, and the continuum it offers between single-view and multi-view sketch-based modeling.
13 code implementations • ICML 2017 • Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, Trevor Darrell
In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether.
1 code implementation • 8 May 2017 • Ting-Chun Wang, Jun-Yan Zhu, Nima Khademi Kalantari, Alexei A. Efros, Ravi Ramamoorthi
Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps.
3 code implementations • 8 May 2017 • Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros
The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).
no code implementations • CVPR 2017 • Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, Jitendra Malik
We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view.
187 code implementations • ICCV 2017 • Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
Ranked #1 on Image-to-Image Translation on zebra2horse (Frechet Inception Distance metric)
Multimodal Unsupervised Image-To-Image Translation Style Transfer +2
4 code implementations • CVPR 2017 • Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, Jitendra Malik
We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives.
2 code implementations • CVPR 2017 • Richard Zhang, Phillip Isola, Alexei A. Efros
We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning.
Ranked #127 on Self-Supervised Image Classification on ImageNet
Representation Learning Self-Supervised Image Classification +1
176 code implementations • CVPR 2017 • Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
1 code implementation • 12 Sep 2016 • Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros
Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result.
1 code implementation • 30 Aug 2016 • Minyoung Huh, Pulkit Agrawal, Alexei A. Efros
Which is better: more classes or more examples per class?
no code implementations • 24 Aug 2016 • Ting-Chun Wang, Jun-Yan Zhu, Ebi Hiroaki, Manmohan Chandraker, Alexei A. Efros, Ravi Ramamoorthi
We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field.
no code implementations • CVPR 2016 • Ting-Chun Wang, Manmohan Chandraker, Alexei A. Efros, Ravi Ramamoorthi
Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture.
4 code implementations • 11 May 2016 • Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, Alexei A. Efros
We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints.
11 code implementations • CVPR 2016 • Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros
In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s).
no code implementations • CVPR 2016 • Tinghui Zhou, Philipp Krähenbühl, Mathieu Aubry, Qi-Xing Huang, Alexei A. Efros
We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth.
39 code implementations • 28 Mar 2016 • Richard Zhang, Phillip Isola, Alexei A. Efros
We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result.
Ranked #128 on Self-Supervised Image Classification on ImageNet
no code implementations • ICCV 2015 • Ting-Chun Wang, Alexei A. Efros, Ravi Ramamoorthi
In this paper, we develop a depth estimation algorithm that treats occlusion explicitly; the method also enables identification of occlusion edges, which may be useful in other applications.
2 code implementations • 9 Nov 2015 • Shiry Ginosar, Kate Rakelly, Sarah Sachs, Brian Yin, Crystal Lee, Philipp Krahenbuhl, Alexei A. Efros
4) A new method for discovering and displaying the visual elements used by the CNN-based date-prediction model to date portraits, finding that they correspond to the tell-tale fashions of each era.
no code implementations • ICCV 2015 • Tinghui Zhou, Philipp Krähenbühl, Alexei A. Efros
We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image.
1 code implementation • ICCV 2015 • Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros
What makes an image appear realistic?
3 code implementations • ICCV 2015 • Carl Doersch, Abhinav Gupta, Alexei A. Efros
This work explores the use of spatial context as a source of free and plentiful supervisory signal for training a rich visual representation.
no code implementations • CVPR 2014 • Mathieu Aubry, Daniel Maturana, Alexei A. Efros, Bryan C. Russell, Josef Sivic
This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online.
no code implementations • NeurIPS 2013 • Carl Doersch, Abhinav Gupta, Alexei A. Efros
We also propose the Purity-Coverage plot as a principled way of experimentally analyzing and evaluating different visual discovery approaches, and compare our method against prior work on the Paris Street View dataset.
no code implementations • ECCV 2012 • Frank Palermo, James Hays, Alexei A. Efros
We introduce the task of automatically estimating the age of historical color photographs.