Search Results for author:
Found 18 papers, 11 papers with code
Three ways to improve feature alignment for open vocabulary detection
The core problem in zero-shot open vocabulary detection is how to align visual and text features, so that the detector performs well on unseen classes.
Object discovery and representation networks
The promise of self-supervised learning (SSL) is to leverage large amounts of unlabeled data to solve complex tasks.
Input-level Inductive Biases for 3D Reconstruction
Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases.
NeRF in detail: Learning to sample for view synthesis
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.
Self-Supervised MultiModal Versatile Networks
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.
Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions
In this work we target the problem of estimating accurately localised correspondences between a pair of images.
Compact Deep Aggregation for Set Retrieval
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
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.
Object Discovery with a Copy-Pasting GAN
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.
Neighbourhood Consensus Networks
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)
GhostVLAD for set-based face recognition
The objective of this paper is to learn a compact representation of image sets for template-based face recognition.
Ranked #3 on
Face Verification
on IJB-A
End-to-end weakly-supervised semantic alignment
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.
Objects that Sound
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.
Look, Listen and Learn
We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos?
Ranked #27 on
Audio Classification
on ESC-50
Convolutional neural network architecture for geometric matching
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.
Pairwise Quantization
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.
NetVLAD: CNN architecture for weakly supervised place recognition
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.
Ranked #3 on
Visual Place Recognition
on Mid-Atlantic Ridge
Three things everyone should know to improve object retrieval
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].
Ranked #6 on
Image Matching
on IMC PhotoTourism
(using extra training data)
