首页|Shandong University Reports Findings in Artificial Intelligence (Human attention guided explainable artificial intelligence for computer vision models)
Shandong University Reports Findings in Artificial Intelligence (Human attention guided explainable artificial intelligence for computer vision models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Jinan, People’s R epublic of China, by NewsRx journalists, research stated, “Explainable artificia l intelligence (XAI) has been increasingly investigated to enhance the transpare ncy of black-box artificial intelligence models, promoting better user understan ding and trust. Developing an XAI that is faithful to models and plausible to us ers is both a necessity and a challenge.” The news correspondents obtained a quote from the research from Shandong Univers ity, “This work examines whether embedding human attention knowledge into salien cy-based XAI methods for computer vision models could enhance their plausibility and faithfulness. Two novel XAI methods for object detection models, namely Ful lGrad-CAM and FullGrad-CAM++, were first developed to generate object-specific e xplanations by extending the current gradient-based XAI methods for image classi fication models. Using human attention as the objective plausibility measure, th ese methods achieve higher explanation plausibility. Interestingly, all current XAI methods when applied to object detection models generally produce saliency m aps that are less faithful to the model than human attention maps from the same object detection task. Accordingly, human attention-guided XAI (HAG-XAI) was pro posed to learn from human attention how to best combine explanatory information from the models to enhance explanation plausibility by using trainable activatio n functions and smoothing kernels to maximize the similarity between XAI salienc y map and human attention map. The proposed XAI methods were evaluated on widely used BDD-100K, MS-COCO, and ImageNet datasets and compared with typical gradien t-based and perturbation-based XAI methods.”
JinanPeople’s Republic of ChinaAsiaArtificial IntelligenceEmerging TechnologiesMachine Learning
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