Other

Shenzhen Malong Technologies Co., Ltd.

Haozhi Zhang

Matthew Robert Scott

Weilin Huang

Sheng Guo

Dinglong Huang

Le Yin

Yu Gao

Tiangang Zhang

Chenfan Zhuang

Dengke Dong

Methods and systems for training machine vision models (MVMs) with 'noisy' training datasets are described. A noisy set of images is received, where labels for some of the images are 'noisy' and/or incorrect. A progressively-sequenced learning curriculum is designed for the noisy dataset, where the images that are easiest to learn machine-vision knowledge from are sequenced near the beginning of the curriculum and images that are harder to learn machine-vision knowledge from are sequenced later in the curriculum. An MVM is trained via providing the sequenced curriculum to a supervised learning method, so that the MVM learns from the easiest examples first and the harder training examples later, i.e., the MVM progressively accumulates knowledge from simplest to most complex. To sequence the curriculum, the training images are embedded in a feature space and the &#x201c;complexity&#x201d; of each image is determined via density distributions and clusters in the feature space.

Complexity-based progressive training for machine vision models

Aspects of this disclosure include technologies to detect unpackaged, unlabeled, or mislabeled products based on product images. Leveraging from improved machine learning techniques, the disclosed technical solution can reduce a full product space for product search to a partial product space. Accordingly, a limited number of product candidates in the partial product space may be visually presented for user evaluation. Sometimes, a product candidate is to be comparatively presented with a live image of the product via a uniquely designed graphic user interface, which further improves the confidence of the user and the accuracy of the underlying transaction.

Compressed network for product recognition

Aspects of this disclosure include technologies for detecting mislabeled products. In one embodiment, the disclosed system will capture an image of a product when the MRL of the product is scanned or being scanned. After recognizing the product in the image, the size of the area containing the product may be calculated. Subsequently, the disclosed system can determine whether the MRL mismatches the product in the image if this size of the area containing the product does not match the standard size associated with the MRL.

Mislabeled product detection

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Shenzhen, China

Haozhi Zhang