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Works319 from public data
- Deep Face Recognition5,690
It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
- Fully-Convolutional Siamese Networks for Object Tracking4,457
A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks.
- Deep Image Prior3,961
It is shown that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting.
- Describing Textures in the Wild3,857
This work identifies a vocabulary of forty-seven texture terms and uses them to describe a large dataset of patterns collected "in the wild", and shows that they both outperform specialized texture descriptors not only on this problem, but also in established material recognition datasets.
- Vlfeat3,535
VLFeat is an open and portable library of computer vision algorithms that includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization.
- Return of the Devil in the Details: Delving Deep into Convolutional Nets3,509
It is shown that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost, and it is identified that the dimensionality of the CNN output layer can be reduced significantly without having an adverse effect on performance.
- MatConvNet2,984
MatConvNet exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing convolutions with filter banks, feature pooling, normalisation, and much more.
- 2,774
- Understanding deep image representations by inverting them2,127
This paper conducts a direct analysis of the visual information contained in representations by asking the following question: given an encoding of an image, to which extent is it possible to reconstruct the image itself?
- VGGT: Visual Geometry Grounded Transformer1,963
VGGT is presented, a feed-forward neural network that directly infers all key 3D attributes of a scene, including camera parameters, point maps, depth maps, and 3D point tracks, from one, a few, or hundreds of its views, a step forward in 3D computer vision.
- Synthetic Data for Text Localisation in Natural Images1,584
The relation of FCRN to the recently-introduced YOLO detector, as well as other end-to-end object detection systems based on deep learning, are discussed.
- Speeding up Convolutional Neural Networks with Low Rank Expansions1,583
Two simple schemes for drastically speeding up convolutional neural networks are presented, achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain.
- 1,519
- End-to-End Representation Learning for Correlation Filter Based Tracking1,469
This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the Cor correlation filter.
- Reading Text in the Wild with Convolutional Neural Networks1,189
An end-to-end system for text spotting—localising and recognising text in natural scene images—and text based image retrieval and a real-world application to allow thousands of hours of news footage to be instantly searchable via a text query is demonstrated.
- 1,084
- 1,054
- Invariant Information Clustering for Unsupervised Image Classification and Segmentation1,005
A novel clustering objective is presented that learns a neural network classifier from scratch, given only unlabelled data samples, and discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation.
- Quick Shift and Kernel Methods for Mode Seeking1,005
It is shown that the complexity of the recently introduced medoid-shift algorithm in clustering N points is O(N 2), with a small constant, if the underlying distance is Euclidean, which makes medoid shift considerably faster than mean shift, contrarily to what previously believed.
- Vggsound: A Large-Scale Audio-Visual Dataset992
The goal is to collect a large-scale audio-visual dataset with low label noise from videos ‘in the wild’ using computer vision techniques and investigates various Convolutional Neural Network architectures and aggregation approaches to establish audio recognition baselines for this new dataset.
- The devil is in the details: an evaluation of recent feature encoding methods954
A rigorous evaluation of novel encodings for bag of visual words models by identifying both those aspects of each method which are particularly important to achieve good performance, and those aspects which are less critical, which allows a consistent comparative analysis of these encoding methods.
- HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors913
A novel benchmark for evaluating local image descriptors is proposed and it is shown that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation.
- 882
- Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis877
This work introduces an instance normalization module to replace batch normalization with significant improvements to the quality of image stylization and improves diversity by introducing a new learning formulation that encourages generators to sample unbiasedly from the Julesz texture ensemble.
- 872
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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