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Found 4 papers, 4 papers with code
DeViL: Decoding Vision features into Language
We demonstrate that DeViL generates textual descriptions relevant to the image content on CC3M surpassing previous lightweight captioning models and attribution maps uncovering the learned concepts of the vision backbone.
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
These findings demonstrate that LLMs are capable of taking on diverse roles and that this in-context impersonation can be used to uncover their hidden strengths and biases.
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?
This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.
Understanding the decisions of CNNs: An in-model approach
The model is trained end-to-end, with the classifier taking as input an image and the explainer’s resulting explanation, thus allowing for the classifier to focus on the relevant areas of such explanation.
