activity
20162026
most citedTransformers in Vision: A Survey

3.6k citations · 10.7k across the 127 of their papers we have counts for

collaborators
Showing 2016Show all

7 papers · 1 filter

cs.CV2016★ 9 cited

Deep Motion Features for Visual Tracking

Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan +1

Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or…

cs.CV2016★ 25 cited

Scale Coding Bag of Deep Features for Human Attribute and Action Recognition

Fahad Shahbaz Khan, Joost van de Weijer, Rao Muhammad Anwer +3

Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a singl…

cs.CV2016★ 23 cited

ECO: Efficient Convolution Operators for Tracking

Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan +1

In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing trac…

cs.CV2016★ 3 cited

Discriminative Scale Space Tracking

Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan +1

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the t…

cs.CV2016★ 36 cited

Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking

Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan +1

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set.…

cs.CV2016★ 1.8k cited

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Martin Danelljan, Andreas Robinson, Fahad Shahbaz Khan +1

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit availabl…

cs.CV2016★ 1.8k cited

Learning Spatially Regularized Correlation Filters for Visual Tracking

Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan +1

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target…