Search Results for author: Relja Arandjelović

Found 17 papers, 10 papers with code

Input-level Inductive Biases for 3D Reconstruction

no code implementations CVPR 2022 Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, Andrew Zisserman

Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases.

3D Reconstruction Depth Estimation

NeRF in detail: Learning to sample for view synthesis

no code implementations9 Jun 2021 Relja Arandjelović, Andrew Zisserman

In this work we address a clear limitation of the vanilla coarse-to-fine approach -- that it is based on a heuristic and not trained end-to-end for the task at hand.

Novel View Synthesis

Self-Supervised MultiModal Versatile Networks

1 code implementation NeurIPS 2020 Jean-Baptiste Alayrac, Adrià Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman

In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.

Action Recognition In Videos Audio Classification +2

Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions

1 code implementation ECCV 2020 Ignacio Rocco, Relja Arandjelović, Josef Sivic

In this work we target the problem of estimating accurately localised correspondences between a pair of images.

Compact Deep Aggregation for Set Retrieval

no code implementations26 Mar 2020 Yujie Zhong, Relja Arandjelović, Andrew Zisserman

The objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the individual descriptors.

Controllable Attention for Structured Layered Video Decomposition

no code implementations ICCV 2019 Jean-Baptiste Alayrac, João Carreira, Relja Arandjelović, Andrew Zisserman

The objective of this paper is to be able to separate a video into its natural layers, and to control which of the separated layers to attend to.

Action Recognition Reflection Removal

Object Discovery with a Copy-Pasting GAN

1 code implementation27 May 2019 Relja Arandjelović, Andrew Zisserman

We tackle the problem of object discovery, where objects are segmented for a given input image, and the system is trained without using any direct supervision whatsoever.

Object Discovery Unsupervised Object Segmentation

Neighbourhood Consensus Networks

3 code implementations NeurIPS 2018 Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović, Akihiko Torii, Tomas Pajdla, Josef Sivic

Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.

Ranked #2 on Semantic correspondence on PF-PASCAL (PCK (weak) metric)

Semantic correspondence Visual Localization

GhostVLAD for set-based face recognition

3 code implementations23 Oct 2018 Yujie Zhong, Relja Arandjelović, Andrew Zisserman

The objective of this paper is to learn a compact representation of image sets for template-based face recognition.

Face Recognition Face Verification

End-to-end weakly-supervised semantic alignment

2 code implementations CVPR 2018 Ignacio Rocco, Relja Arandjelović, Josef Sivic

We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category.

Semantic correspondence

Objects that Sound

no code implementations ECCV 2018 Relja Arandjelović, Andrew Zisserman

We make the following contributions: (i) show that audio and visual embeddings can be learnt that enable both within-mode (e. g. audio-to-audio) and between-mode retrieval; (ii) explore various architectures for the AVC task, including those for the visual stream that ingest a single image, or multiple images, or a single image and multi-frame optical flow; (iii) show that the semantic object that sounds within an image can be localized (using only the sound, no motion or flow information); and (iv) give a cautionary tale on how to avoid undesirable shortcuts in the data preparation.

Cross-Modal Retrieval Optical Flow Estimation

Look, Listen and Learn

1 code implementation ICCV 2017 Relja Arandjelović, Andrew Zisserman

We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos?

Audio Classification General Classification

Convolutional neural network architecture for geometric matching

5 code implementations CVPR 2017 Ignacio Rocco, Relja Arandjelović, Josef Sivic

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters.

Geometric Matching

Pairwise Quantization

no code implementations5 Jun 2016 Artem Babenko, Relja Arandjelović, Victor Lempitsky

The proposed approach proceeds by finding a linear transformation of the data that effectively reduces the minimization of the pairwise distortions to the minimization of individual reconstruction errors.

Quantization

NetVLAD: CNN architecture for weakly supervised place recognition

15 code implementations CVPR 2016 Relja Arandjelović, Petr Gronat, Akihiko Torii, Tomas Pajdla, Josef Sivic

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph.

Image Retrieval Visual Place Recognition

Three things everyone should know to improve object retrieval

1 code implementation CVPR 2012 Relja Arandjelović, Andrew Zisserman

The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28].

Image Augmentation Image Matching

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