计算机视觉与图像理解CVIU(Computer Vision and Image Understanding)的中心内容是对图像信息的计算机分析。这本杂志发表的论文涵盖了图像分析的各个方面,从早期视觉的低级、形象性过程,到识别和解释的高级、符号化过程。图像理解领域的广泛主题被覆盖,包括提供与主流观点不同的见解的论文。 官网地址:http://dblp.uni-trier.de/db/journals/cviu/

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Currently available methods for extracting saliency maps identify parts of the input which are the most important to a specific fixed classifier. We show that this strong dependence on a given classifier hinders their performance. To address this problem, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance. We observe that the proposed approach extracts higher quality saliency maps than prior work while being conceptually simple and easy to implement. The method sets the new state of the art result for localization task on the ImageNet data, outperforming all existing weakly-supervised localization techniques, despite not using the ground truth labels at the inference time. The code reproducing the results is available at https://github.com/kondiz/casme . The final version of this manuscript is published in Computer Vision and Image Understanding and is available online at https://doi.org/10.1016/j.cviu.2020.102969 .

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