{'fraction_non_alphanumeric': 0.02292490118577075, 'fraction_numerical': 0.0047430830039525695, 'mean_word_length': 5.5595854922279795, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multi-attribute Classification of Human Body Images Based on Simplified Two-level Consistency

Multi-attribute classification of human body images is an important research topic in computer vision. It belongs to a type of multi-label classification, which mainly classifies various attributes of people. The attention mechanism is a common method for solving the multi-label classification problem, in which the visual attention consistency method can achieve good performance. However, this kind of visual attention consistency requires manually designing complex mapping relationships between attention graphs. This paper proposes a multi-attribute classification of human body images based on simplified two-level consistency. We first propose a logits consistency matching mechanism, which does not require the manual design of mapping relationships between complex attention graphs and reduces the difficulty to achieve consistency. Afterward, based on this logits consistency, a complete two-level consistency mechanism is designed to further improve the accuracy of the model. In experiments on the multi-attribute classification of human body images benchmarks, the proposed two-level consistency method achieves a mean accuracy of 92.7% and a mAP of 87.1%.