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Found 14 papers, 5 papers with code
Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes
Fine-tuning is ineffective for few-shot learning, since the target dataset contains only a handful of examples.
On progressive sharpening, flat minima and generalisation
We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning.
Hierarchical classification at multiple operating points
Many classification problems consider classes that form a hierarchy.
On skip connections and normalisation layers in deep optimisation
We introduce a general theoretical framework, designed for the study of gradient optimisation of deep neural networks, that encompasses ubiquitous architecture choices including batch normalisation, weight normalisation and skip connections.
Learning with Neighbor Consistency for Noisy Labels
Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models.
Local Metrics for Multi-Object Tracking
This paper introduces temporally local metrics for Multi-Object Tracking.
Long-term Tracking in the Wild: A Benchmark
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms.
Devon: Deformable Volume Network for Learning Optical Flow
State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions.
End-to-end representation learning for Correlation Filter based tracking
The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations.
Ranked #3 on
Visual Object Tracking
on OTB-50
Fully-Convolutional Siamese Networks for Object Tracking
The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.
Ranked #3 on
Visual Object Tracking
on OTB-50
Learning feed-forward one-shot learners
In this paper, we propose a method to learn the parameters of a deep model in one shot.
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.
Ranked #28 on
Visual Object Tracking
on TrackingNet
Dense Semantic Correspondence where Every Pixel is a Classifier
Determining dense semantic correspondences across objects and scenes is a difficult problem that underpins many higher-level computer vision algorithms.
Learning detectors quickly using structured covariance matrices
Computer vision is increasingly becoming interested in the rapid estimation of object detectors.
