: The standard classification architectures are designed and trained for obtaining impressive performance on dedicated image classification datasets, which usually contain images with a single object located at the image center. However, their accuracy drops when this assumption is violated, e.g., if the target object is cluttered with background noise or if it is not centered. In this paper, we study salient object classification: a more realistic scenario where there are multiple object instances in the scene, and we are interested in classifying the image based on the label corresponding to the most salient object. Inspired by previous works on Rein-forcement Learning and Spatial Transformer Networks, we propose a model equipped with a trainable focus mechanism, which improves classification accuracy. Our experiments on the PASCAL VOC dataset show that the method is capable of increasing the intersection-ver-union of the salient object, which improves the classification accuracy by 1.82 pp overall, and 3.63 pp for smaller objects. We provide an analysis of the failing cases, discussing different aspects such as dataset bias and saliency definition on the classification output.

Sequential Spatial Transformer Networks for Salient Object Classification

A restriction map of bacteriophage t3

The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model's decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerous model evaluations and introduces uncertainty due to random sampling. This work proposes a novel metric for evaluating the fidelity of local feature attribution methods by modifying the existing Prediction Change (PC) metric within the Guided Perturbation Experiment. By incorporating the direction of both perturbation and attribution, the proposed Directed Prediction Change (DPC) metric achieves an almost tenfold speedup and eliminates randomness, resulting in a deterministic and trustworthy evaluation procedure that measures the same property as local Infidelity. DPC is evaluated on two datasets (skin lesion images and financial tabular data), two black-box models, seven explanation algorithms, and a wide range of hyperparameters. Across $4\,744$ distinct explanations, the results demonstrate that DPC, together with PC, enables a holistic and computationally efficient evaluation of both baseline-oriented and local feature attribution methods, while providing deterministic and reproducible outcomes.

https://export.arxiv.org/pdf/2511.21363v1.pdf

The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods

Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI

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Sequential Spatial Transformer Networks for Salient Object Classification

A restriction map of bacteriophage t3

The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods

Unifying VXAI: A Systematic Review and Framework for the Evaluation of Explainable AI