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Found 12 papers, 5 papers with code
Measuring the Importance of Temporal Features in Video Saliency
Where people look when watching videos is believed to be heavily influenced by temporal patterns.
RDumb: A simple approach that questions our progress in continual test-time adaptation
Test-Time Adaptation (TTA) allows to update pre-trained models to changing data distributions at deployment time.
Disentanglement and Generalization Under Correlation Shifts
Exploiting such correlations may increase predictive performance on noisy data; however, often correlations are not robust (e. g., they may change between domains, datasets, or applications) and models that exploit them do not generalize when correlations shift.
Unsupervised Object Learning via Common Fate
Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling.
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling
Since 2014 transfer learning has become the key driver for the improvement of spatial saliency prediction; however, with stagnant progress in the last 3-5 years.
State-of-the-Art in Human Scanpath Prediction
The last years have seen a surge in models predicting the scanpaths of fixations made by humans when viewing images.
Accurate, reliable and fast robustness evaluation
We here develop a new set of gradient-based adversarial attacks which (a) are more reliable in the face of gradient-masking than other gradient-based attacks, (b) perform better and are more query efficient than current state-of-the-art gradient-based attacks, (c) can be flexibly adapted to a wide range of adversarial criteria and (d) require virtually no hyperparameter tuning.
Guiding human gaze with convolutional neural networks
Thus, manipulating fixation patterns to guide human attention is an exciting challenge in digital image processing.
Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics
Here we show that no single saliency map can perform well under all metrics.
DeepGaze II: Reading fixations from deep features trained on object recognition
Here we present DeepGaze II, a model that predicts where people look in images.
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
Recent results suggest that state-of-the-art saliency models perform far from optimal in predicting fixations.
How close are we to understanding image-based saliency?
Within the set of the many complex factors driving gaze placement, the properities of an image that are associated with fixations under free viewing conditions have been studied extensively.
