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Found 4 papers, 2 papers with code
Implicit and Explicit Attention for Zero-Shot Learning
Most of the existing Zero-Shot Learning (ZSL) methods focus on learning a compatibility function between the image representation and class attributes.
Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to recognise unseen object classes, which are not observed during the training phase.
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection
Deep neural network approaches have demonstrated high performance in object recognition (CNN) and detection (Faster-RCNN) tasks, but experiments have shown that such architectures are vulnerable to adversarial attacks (FFF, UAP): low amplitude perturbations, barely perceptible by the human eye, can lead to a drastic reduction in labeling performance.
Contextual Relabelling of Detected Objects
Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes.
