429 citations · 880 across the 60 of their papers we have counts for
77 papers · 1 filter
StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
Emanuele Bugliarello, Hernan Moraldo, Ruben Villegas +7
Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst be…
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories
Thomas Mensink, Jasper Uijlings, Lluis Castrejon +6
We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. It…
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt +13
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on…
Estimating Generic 3D Room Structures from 2D Annotations
Denys Rozumnyi, Stefan Popov, Kevis-Kokitsi Maninis +2
Indoor rooms are among the most common use cases in 3D scene understanding. Current state-of-the-art methods for this task are driven by large annotated datasets. Room layouts are…
CAD-Estate: Large-scale CAD Model Annotation in RGB Videos
Kevis-Kokitsi Maninis, Stefan Popov, Matthias Nießner +1
We propose a method for annotating videos of complex multi-object scenes with a globally-consistent 3D representation of the objects. We annotate each object with a CAD model from…
Tracking by 3D Model Estimation of Unknown Objects in Videos
Denys Rozumnyi, Jiri Matas, Marc Pollefeys +2
Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue…
Connecting Vision and Language with Video Localized Narratives
Paul Voigtlaender, Soravit Changpinyo, Jordi Pont-Tuset +2
We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives, annotators speak and move th…
Beyond SOT: Tracking Multiple Generic Objects at Once
Christoph Mayer, Martin Danelljan, Ming-Hsuan Yang +3
Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in th…
From colouring-in to pointillism: revisiting semantic segmentation supervision
Rodrigo Benenson, Vittorio Ferrari
The prevailing paradigm for producing semantic segmentation training data relies on densely labelling each pixel of each image in the training set, akin to colouring-in books. This…
Multi-View Photometric Stereo Revisited
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. Although popular methods for MVPS can provide outstandi…
The Missing Link: Finding label relations across datasets
Jasper Uijlings, Thomas Mensink, Vittorio Ferrari
Computer vision is driven by the many datasets available for training or evaluating novel methods. However, each dataset has a different set of class labels, visual definition of c…
How stable are Transferability Metrics evaluations?
Andrea Agostinelli, Michal Pándy, Jasper Uijlings +2
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…
Uncertainty-Aware Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Carlos Oliveira +2
This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is exc…
RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformers
Michał J. Tyszkiewicz, Kevis-Kokitsi Maninis, Stefan Popov +1
We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos. It relies on two alternative ways to represent its knowledge: as a gl…
Urban Radiance Fields
Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan +4
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 environ…
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari +1
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a…
Transferability Metrics for Selecting Source Model Ensembles
Andrea Agostinelli, Jasper Uijlings, Thomas Mensink +1
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 targe…
Transferability Estimation using Bhattacharyya Class Separability
Michal Pándy, Andrea Agostinelli, Jasper Uijlings +2
Transfer learning has become a popular method for leveraging pre-trained models in computer vision. However, without performing computationally expensive fine-tuning, it is difficu…
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
Francesco Sarno, Suryansh Kumar, Berk Kaya +3
We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural network…
Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Francesco Sarno +2
We present a modern solution to the multi-view photometric stereo problem (MVPS). Our work suitably exploits the image formation model in a MVPS experimental setup to recover the d…
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari +1
We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image. While previous approaches address the deblurr…
A Step Toward More Inclusive People Annotations for Fairness
Candice Schumann, Susanna Ricco, Utsav Prabhu +2
The Open Images Dataset contains approximately 9 million images and is a widely accepted dataset for computer vision research. As is common practice for large datasets, the annotat…
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types
Thomas Mensink, Jasper Uijlings, Alina Kuznetsova +2
Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art comp…
ShaRF: Shape-conditioned Radiance Fields from a Single View
Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari
We present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the obje…