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Found 12 papers, 8 papers with code
Detecting and Matching Related Objects with One Proposal Multiple Predictions
Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time.
Group Activity Detection from Trajectory and Video Data in Soccer
Group activity detection in soccer can be done by using either video data or player and ball trajectory data.
Meta-learning for fast classifier adaptation to new users of Signature Verification systems
This is particularly challenging for skilled forgeries, where a forger practices imitating the user's signature, and often is able to create forgeries visually close to the original signatures.
Universal Adversarial Audio Perturbations
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios.
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification
The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning systems.
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implications for a wide range of image processing systems.
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Methods for learning feature representations for Offline Handwritten Signature Verification have been successfully proposed in recent literature, using Deep Convolutional Neural Networks to learn representations from signature pixels.
DESlib: A Dynamic ensemble selection library in Python
DESlib is an open-source python library providing the implementation of several dynamic selection techniques.
Learning Features for Offline Handwritten Signature Verification using Deep Convolutional Neural Networks
Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it.
Analyzing features learned for Offline Signature Verification using Deep CNNs
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points.
Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others.
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem.
