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Found 7 papers, 1 papers with code
Extreme Image Transformations Facilitate Robust Latent Object Representations
Adversarial attacks can affect the object recognition capabilities of machines in wild.
Extreme Image Transformations Affect Humans and Machines Differently
We introduce a set of novel image transforms inspired by neurophysiological findings and evaluate humans and ANNs on an object recognition task.
Solving Royal Game of Ur Using Reinforcement Learning
Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received.
Robustness of Humans and Machines on Object Recognition with Extreme Image Transformations
Recent neural network architectures have claimed to explain data from the human visual cortex.
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks
Here, we introduce $\textit{PathTracker}$, a visual challenge inspired by cognitive psychology, which tests the ability of observers to learn to track objects solely by their motion.
Tracking Without Re-recognition in Humans and Machines
For this, we introduce PathTracker, a synthetic visual challenge that asks human observers and machines to track a target object in the midst of identical-looking "distractor" objects.
Little Motion, Big Results: Using Motion Magnification to Reveal Subtle Tremors in Infants
Detecting tremors is challenging for both humans and machines.
