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Works82 from public data
- 5,739
- 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.
- Exploiting the Circulant Structure of Tracking-by-Detection with Kernels2,365
Using the well-established theory of Circulant matrices, this work provides a link to Fourier analysis that opens up the possibility of extremely fast learning and detection with the Fast Fourier Transform, which can be done in the dual space of kernel machines as fast as with linear classifiers.
- 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.
- 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.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
- Learning feed-forward one-shot learners500
This paper constructs the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar, and obtains an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one- shot classification objective in a learning to learn formulation.
- 434
- Multi-modal Self-Supervision from Generalized Data Transformations197
The framework of Generalized Data Transformations is introduced to reduce several recent self-supervised learning objectives to a single formulation for ease of comparison, analysis, and extension, and allow a choice between being invariant or distinctive to data transformations, obtaining different supervisory signals, and derive the conditions that combinations of transformations must obey in order to lead to well-posed learning objectives.
- 186
- Long-Term Tracking in the Wild: A Benchmark184
The OxUvA dataset and benchmark for evaluating single-object tracking algorithms is introduced, offering the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used “in the wild”.
- 149
- Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image145
This work proposes Flash3D, a method for scene reconstruction and novel view synthesis from a single image which is both very generalisable and efficient, and achieves state-of-the-art results when trained and tested on RealEstate10k.
- 145
- Audio Retrieval With Natural Language Queries: A Benchmark Study137
This work employs three challenging new benchmarks to establish baselines for cross-modal text-audio and audio-text retrieval, and introduces the SoundDescs benchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found in AudioCaps and Clotho.
- 113
- Audio Retrieval with Natural Language Queries93
This work introduces challenging new benchmarks for text-based audio retrieval using text annotations sourced from the Audiocaps and Clotho datasets and employs these benchmarks to establish baselines for cross-modal audio retrieval, where the benefits of pre-training on diverse audio tasks are demonstrated.
- RbA: Segmenting Unknown Regions Rejected by All88
This paper proposes a novel outlier scoring function called RbA by defining the event of being an outlier as being rejected by all known classes, and shows that mask classification improves the performance of the existing outlier detection methods, and the best results are achieved with the proposed RbA.
- CASSPR: Cross Attention Single Scan Place Recognition83
CASSPR is proposed as a method to fuse point-based and voxel-based approaches using cross attention transformers using cross attention transformers, and surpasses the state-of-the-art by a large margin on several datasets.
- Text2Loc: 3D Point Cloud Localization from Natural Language81
This work introduces a novel neural network, Text2Loc, that fully interprets the semantic relationship between points and text and proposes a novel matching-free fine localization method, which completely removes the need for complicated text-instance matching and is lighter, faster, and more accurate than previous methods.
- Invariant Information Distillation for Unsupervised Image Segmentation and Clustering69
A simple loss based on information theory is used to extract meaningful representations directly from raw images, making it the first end-to-end unsupervised learning method that learns densely and efficiently (i.e. without sampling) for semantic segmentation.
- VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning66
Neuro-Symbolic Predicates is presented, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations and an online algorithm for inventing such predicates and learning abstract world models is outlined.
- Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow58
- On Compositions of Transformations in Contrastive Self-Supervised Learning54
It is found that being invariant to certain transformations and distinctive to others is critical to learning effective video representations, improving the state-of-the-art for multiple benchmarks by a large margin, and even surpassing supervised pretraining.
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 2026-10-14. 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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