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Found 4 papers, 0 papers with code
Reinforcement Learning for Visual Object Detection
One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search.
Multiple Instance Reinforcement Learning for Efficient Weakly-Supervised Detection in Images
State-of-the-art visual recognition and detection systems increasingly rely on large amounts of training data and complex classifiers.
Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition
Systems based on bag-of-words models from image features collected at maxima of sparse interest point operators have been used successfully for both computer visual object and action recognition tasks.
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths
Finally, we leverage our large scale dataset in conjunction with powerful machine learning techniques and computer vision features, to introduce novel dynamic eye movement prediction methods which learn task-sensitive reward functions from eye movement data and efficiently integrate these rewards to plan future saccades based on inverse optimal control.
