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Found 6 papers, 1 papers with code
OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery
Our method also excels in low-data settings, outperforming supervised baselines using a fraction of the training data.
Tunable Hybrid Proposal Networks for the Open World
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes.
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks.
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers.
Snooping Attacks on Deep Reinforcement Learning
In these snooping threat models, the adversary does not have the ability to interact with the target agent's environment, and can only eavesdrop on the action and reward signals being exchanged between agent and environment.
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video domains.
