Several queries and scores have been proposed to explain individual predictions made by ML models. Examples include queries based on "anchors", which are parts of an instance that are sufficient to justify its classification, and "feature-perturbation" scores such as SHAP .

Machine learning models and algorithms appear more and more frequently in systems that make decisions with an impact in our lives.

This observation suggests that attention positions should not be treated the same in dropout.

Recent work has demonstrated that by leveraging double visual and answer hints, a model can faithfully generate much better quality questions.

Our novel I2DFormer which only utilizes the I2DAttention module, outperforms all these baselines in row d). Our model is designed with the problem constraints of our ZSL setting and the resulting information asymmetry in mind.

Despite the tremendous progress in zero-shot learning (ZSL), the majority of existing methods still rely on human-annotated attributes, which are difficult to annotate and scale. An unsupervised alternative is to represent each class using the word embedding associated with its semantic class name.

For the GP, the bias-variance trade-off is made by optimization of the two hyperparameters: the length scale and noise-term.

Thus, there is a need for a reference point, on which each model can be tested and from where potential improvements can be derived. In this study, we select publicly available state-of-the-art visual search models and datasets in natural scenes, and provide a common framework for their evaluation. To this end, we apply a unified format and criteria, bridging the gaps between them, and we estimate the models' efficiency and similarity with humans using a specific set of metrics.

Lastly, our analysis also shows the consistency of union support recovery of the structures.