Search Results for author: Vittorio Ferrari

Found 77 papers, 16 papers with code

The Missing Link: Finding label relations across datasets

no code implementations9 Jun 2022 Jasper Uijlings, Thomas Mensink, Vittorio Ferrari

Our methods can effectively discover label relations across datasets and the type of the relations.

How stable are Transferability Metrics evaluations?

no code implementations4 Apr 2022 Andrea Agostinelli, Michal Pándy, Jasper Uijlings, Thomas Mensink, Vittorio Ferrari

Transferability metrics is a maturing field with increasing interest, which aims at providing heuristics for selecting the most suitable source models to transfer to a given target dataset, without fine-tuning them all.

Image Classification Semantic Segmentation

Uncertainty-Aware Deep Multi-View Photometric Stereo

no code implementations CVPR 2022 Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc van Gool

At each pixel, our approach either selects or discards deep-PS and deep-MVS network prediction depending on the prediction uncertainty measure.

Surface Reconstruction

Urban Radiance Fields

no code implementations CVPR 2022 Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan, Jonathan T. Barron, Andrea Tagliasacchi, Thomas Funkhouser, Vittorio Ferrari

The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e. g., Street View).

3D Reconstruction Novel View Synthesis

Transferability Metrics for Selecting Source Model Ensembles

no code implementations CVPR 2022 Andrea Agostinelli, Jasper Uijlings, Thomas Mensink, Vittorio Ferrari

We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the target training set, yields the best performance on the target test set.

Semantic Segmentation Transfer Learning

Transferability Estimation using Bhattacharyya Class Separability

no code implementations CVPR 2022 Michal Pándy, Andrea Agostinelli, Jasper Uijlings, Vittorio Ferrari, Thomas Mensink

Then, we estimate their pairwise class separability using the Bhattacharyya coefficient, yielding a simple and effective measure of how well the source model transfers to the target task.

Classification Image Classification +2

Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo

no code implementations11 Oct 2021 Berk Kaya, Suryansh Kumar, Francesco Sarno, Vittorio Ferrari, Luc van Gool

Our method performs neural rendering of multi-view images while utilizing surface normals estimated by a deep photometric stereo network.

3D Reconstruction Neural Rendering

Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo

no code implementations11 Oct 2021 Francesco Sarno, Suryansh Kumar, Berk Kaya, Zhiwu Huang, Vittorio Ferrari, Luc van Gool

We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network.

Neural Architecture Search

Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

1 code implementation NeurIPS 2021 Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys

We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image.

Deblurring Super-Resolution +1

A Step Toward More Inclusive People Annotations for Fairness

no code implementations5 May 2021 Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, Caroline Pantofaru

In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images.

Fairness

Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types

no code implementations24 Mar 2021 Thomas Mensink, Jasper Uijlings, Alina Kuznetsova, Michael Gygli, Vittorio Ferrari

Our study leads to several insights and concrete recommendations: (1) for most tasks there exists a source which significantly outperforms ILSVRC'12 pre-training; (2) the image domain is the most important factor for achieving positive transfer; (3) the source dataset should \emph{include} the image domain of the target dataset to achieve best results; (4) at the same time, we observe only small negative effects when the image domain of the source task is much broader than that of the target; (5) transfer across task types can be beneficial, but its success is heavily dependent on both the source and target task types.

Autonomous Driving Depth Estimation +6

ShaRF: Shape-conditioned Radiance Fields from a Single View

no code implementations17 Feb 2021 Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari

We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.

Disentanglement

Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos

1 code implementation8 Dec 2020 Kevis-Kokitsi Maninis, Stefan Popov, Matthias Nießner, Vittorio Ferrari

We address the task of aligning CAD models to a video sequence of a complex scene containing multiple objects.

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

5 code implementations CVPR 2021 Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys

We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).

Deblurring Object Tracking +1

Efficient Full Image Interactive Segmentation by Leveraging Within-image Appearance Similarity

no code implementations16 Jul 2020 Mykhaylo Andriluka, Stefano Pellegrini, Stefan Popov, Vittorio Ferrari

We leverage a key observation: propagation from labeled to unlabeled pixels does not necessarily require class-specific knowledge, but can be done purely based on appearance similarity within an image.

Interactive Segmentation Semantic Segmentation

C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds

no code implementations CVPR 2020 Albert Pumarola, Stefan Popov, Francesc Moreno-Noguer, Vittorio Ferrari

Flow-based generative models have highly desirable properties like exact log-likelihood evaluation and exact latent-variable inference, however they are still in their infancy and have not received as much attention as alternative generative models.

3D Reconstruction Image Manipulation +1

Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool

no code implementations CVPR 2020 Konstantinos Rematas, Vittorio Ferrari

Finally, we show how our neural rendering framework can capture and faithfully render objects from real images and from a diverse set of classes.

Image Generation Neural Rendering

Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images

no code implementations arXiv 2019 Zhaohui Yang, Miaojing Shi, Chao Xu, Vittorio Ferrari, Yannis Avrithis

Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set.

Ranked #21 on Weakly Supervised Object Detection on PASCAL VOC 2012 test (using extra training data)

object-detection Weakly Supervised Object Detection

Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections

no code implementations ECCV 2020 Theodora Kontogianni, Michael Gygli, Jasper Uijlings, Vittorio Ferrari

Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing.

 Ranked #1 on Interactive Segmentation on Rooftop (NoC@80 metric)

Interactive Segmentation Semantic Segmentation

Panoptic Image Annotation with a Collaborative Assistant

no code implementations17 Jun 2019 Jasper R. R. Uijlings, Mykhaylo Andriluka, Vittorio Ferrari

This paper aims to reduce the time to annotate images for panoptic segmentation, which requires annotating segmentation masks and class labels for all object instances and stuff regions.

Panoptic Segmentation

Natural Vocabulary Emerges from Free-Form Annotations

no code implementations4 Jun 2019 Jordi Pont-Tuset, Michael Gygli, Vittorio Ferrari

This vocabulary represents the natural distribution of objects well and is learned directly from data, instead of being an educated guess done before collecting any labels.

Efficient Object Annotation via Speaking and Pointing

no code implementations25 May 2019 Michael Gygli, Vittorio Ferrari

We then combine the two stages: annotators draw an object bounding box via the mouse and simultaneously provide its class label via speech.

Learning single-image 3D reconstruction by generative modelling of shape, pose and shading

1 code implementation19 Jan 2019 Paul Henderson, Vittorio Ferrari

Importantly, it can be trained purely from 2D images, without pose annotations, and with only a single view per instance.

3D Reconstruction

Interactive Full Image Segmentation by Considering All Regions Jointly

no code implementations CVPR 2019 Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari

We propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions.

Interactive Segmentation Semantic Segmentation

Fast Object Class Labelling via Speech

no code implementations CVPR 2019 Michael Gygli, Vittorio Ferrari

Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC).

Learning to Generate and Reconstruct 3D Meshes with only 2D Supervision

no code implementations24 Jul 2018 Paul Henderson, Vittorio Ferrari

Importantly, it can be trained purely from 2D images, without ground-truth pose annotations, and with a single view per instance.

3D Reconstruction

Detecting Visual Relationships Using Box Attention

no code implementations5 Jul 2018 Alexander Kolesnikov, Alina Kuznetsova, Christoph H. Lampert, Vittorio Ferrari

We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table".

object-detection Object Detection

Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation

no code implementations20 Jun 2018 Mykhaylo Andriluka, Jasper R. R. Uijlings, Vittorio Ferrari

As opposed to performing a series of small annotation tasks in isolation, we propose a unified interface for full image annotation in a single pass.

Learning Intelligent Dialogs for Bounding Box Annotation

1 code implementation CVPR 2018 Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert, Vittorio Ferrari

We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the image difficulty, the desired quality of the boxes, and the detector strength; (2) in all scenarios the resulting annotation dialogs speed up annotation compared to manual box drawing alone and box verification alone, while also outperforming any fixed combination of verification and drawing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between verification and drawing as the detector grows stronger.

Automatic Generation of Constrained Furniture Layouts

no code implementations29 Nov 2017 Paul Henderson, Kartic Subr, Vittorio Ferrari

Efficient authoring of vast virtual environments hinges on algorithms that are able to automatically generate content while also being controllable.

Active Learning for Human Pose Estimation

no code implementations ICCV 2017 Buyu Liu, Vittorio Ferrari

Annotating human poses in realistic scenes is very time consuming, yet necessary for training human pose estimators.

Active Learning Pose Estimation

Revisiting knowledge transfer for training object class detectors

no code implementations CVPR 2018 Jasper Uijlings, Stefan Popov, Vittorio Ferrari

We propose to revisit knowledge transfer for training object detectors on target classes from weakly supervised training images, helped by a set of source classes with bounding-box annotations.

Transfer Learning

Extreme clicking for efficient object annotation

no code implementations ICCV 2017 Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari

We crowd-source extreme point annotations for PASCAL VOC 2007 and 2012 and show that (1) annotation time is only 7s per box, 5x faster than the traditional way of drawing boxes [62]; (2) the quality of the boxes is as good as the original ground-truth drawn the traditional way; (3) detectors trained on our annotations are as accurate as those trained on the original ground-truth.

The Devil is in the Decoder: Classification, Regression and GANs

1 code implementation18 Jul 2017 Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper Uijlings

Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.

Boundary Detection Depth Estimation +2

Action Tubelet Detector for Spatio-Temporal Action Localization

2 code implementations ICCV 2017 Vicky Kalogeiton, Philippe Weinzaepfel, Vittorio Ferrari, Cordelia Schmid

We propose the ACtion Tubelet detector (ACT-detector) that takes as input a sequence of frames and outputs tubelets, i. e., sequences of bounding boxes with associated scores.

Spatio-Temporal Action Localization Temporal Action Localization

Objects as context for detecting their semantic parts

no code implementations CVPR 2018 Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari

We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect.

Semantic Part Detection

Weakly Supervised Object Localization Using Things and Stuff Transfer

no code implementations ICCV 2017 Miaojing Shi, Holger Caesar, Vittorio Ferrari

We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations.

Multiple Instance Learning Weakly-Supervised Object Localization

COCO-Stuff: Thing and Stuff Classes in Context

10 code implementations CVPR 2018 Holger Caesar, Jasper Uijlings, Vittorio Ferrari

To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes.

Image Captioning Semantic Segmentation +1

Learning Semantic Part-Based Models from Google Images

no code implementations11 Sep 2016 Davide Modolo, Vittorio Ferrari

We evaluate our models on the challenging PASCAL-Part dataset [1] and show how their performance increases at every step of the learning, with the final models more than doubling the performance of directly training from images retrieved by querying for part names (from 12. 9 to 27. 2 AP).

object-detection Object Detection

Weakly Supervised Object Localization Using Size Estimates

no code implementations15 Aug 2016 Miaojing Shi, Vittorio Ferrari

We present a technique for weakly supervised object localization (WSOL), building on the observation that WSOL algorithms usually work better on images with bigger objects.

Weakly-Supervised Object Localization

Region-based semantic segmentation with end-to-end training

1 code implementation26 Jul 2016 Holger Caesar, Jasper Uijlings, Vittorio Ferrari

We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class.

Semantic Segmentation

Do semantic parts emerge in Convolutional Neural Networks?

no code implementations13 Jul 2016 Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari

We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network.

Object Recognition

End-to-end training of object class detectors for mean average precision

no code implementations12 Jul 2016 Paul Henderson, Vittorio Ferrari

We present a method for training CNN-based object class detectors directly using mean average precision (mAP) as the training loss, in a truly end-to-end fashion that includes non-maximum suppression (NMS) at training time.

General Classification

Discovering the Physical Parts of an Articulated Object Class From Multiple Videos

no code implementations CVPR 2016 Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

We propose a motion-based method to discover the physical parts of an articulated object class (e. g. head/torso/leg of a horse) from multiple videos.

Motion Segmentation

We don't need no bounding-boxes: Training object class detectors using only human verification

1 code implementation CVPR 2016 Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari

Training object class detectors typically requires a large set of images in which objects are annotated by bounding-boxes.

Automatically selecting inference algorithms for discrete energy minimisation

no code implementations19 Nov 2015 Paul Henderson, Vittorio Ferrari

Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed.

Joint Calibration for Semantic Segmentation

no code implementations6 Jul 2015 Holger Caesar, Jasper Uijlings, Vittorio Ferrari

Semantic segmentation is the task of assigning a class-label to each pixel in an image.

Semantic Segmentation

What's the Point: Semantic Segmentation with Point Supervision

1 code implementation6 Jun 2015 Amy Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei

The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost.

Semantic Segmentation

Context Forest for efficient object detection with large mixture models

no code implementations3 Mar 2015 Davide Modolo, Alexander Vezhnevets, Vittorio Ferrari

We present Context Forest (ConF), a technique for predicting properties of the objects in an image based on its global appearance.

object-detection Object Detection

Joint calibration of Ensemble of Exemplar SVMs

no code implementations CVPR 2015 Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari

We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum.

object-detection Object Detection

An active search strategy for efficient object class detection

no code implementations CVPR 2015 Abel Gonzalez-Garcia, Alexander Vezhnevets, Vittorio Ferrari

First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set.

Recovering Spatiotemporal Correspondence between Deformable Objects by Exploiting Consistent Foreground Motion in Video

no code implementations1 Dec 2014 Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

Given unstructured videos of deformable objects, we automatically recover spatiotemporal correspondences to map one object to another (such as animals in the wild).

Articulated motion discovery using pairs of trajectories

no code implementations CVPR 2015 Luca Del Pero, Susanna Ricco, Rahul Sukthankar, Vittorio Ferrari

We propose an unsupervised approach for discovering characteristic motion patterns in videos of highly articulated objects performing natural, unscripted behaviors, such as tigers in the wild.

Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation

no code implementations27 Mar 2014 Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari, Christoph H. Lampert

It is inspired by existing closed-form expressions for the maximum likelihood parameters of a generative graphical model with tree topology.

Semantic Segmentation

Associative embeddings for large-scale knowledge transfer with self-assessment

no code implementations CVPR 2014 Alexander Vezhnevets, Vittorio Ferrari

By transferring knowledge from the images that have bounding-box annotations to the others, our method is capable of automatically populating ImageNet with many more bounding-boxes and even pixel-level segmentations.

Gaussian Processes Object Localization +1

Fast Energy Minimization Using Learned State Filters

no code implementations CVPR 2013 Matthieu Guillaumin, Luc van Gool, Vittorio Ferrari

However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take.

Searching for objects driven by context

no code implementations NeurIPS 2012 Bogdan Alexe, Nicolas Heess, Yee W. Teh, Vittorio Ferrari

The dominant visual search paradigm for object class detection is sliding windows.

Exploiting spatial overlap to efficiently compute appearance distances between image windows

no code implementations NeurIPS 2011 Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari

We present a computationally efficient technique to compute the distance of high-dimensional appearance descriptor vectors between image windows.

Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation

no code implementations NeurIPS 2009 Jie Luo, Barbara Caputo, Vittorio Ferrari

Given a corpus of news items consisting of images accompanied by text captions, we want to find out ``whos doing what, i. e. associate names and action verbs in the captions to the face and body pose of the persons in the images.

Cannot find the paper you are looking for? You can Submit a new open access paper.