Галерея 2979258
Галерея 2979258
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Object Hallucination From Attribute Pairs
Abstract: Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based gene... View more
Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based generation methods confront a dilemma. On the one hand, the image generator should be provided with sufficient details for realistic hallucination, meaning that longer sentences with rich content are preferred, but on the other hand, the generator is meanwhile fragile to long sentences due to their complex semantics and syntax like long-range dependencies and the combinatorial explosion of object visual features. Toward alleviating this dilemma, a novel approach is proposed in this article to hallucinate images from attribute pairs, which can be extracted from natural language processing (NLP) toolsets in the presence of complex semantics and syntax. Attribute pairs, therefore, enable our image generator to tackle long sentences handily and alleviate the combinatorial explosion, and at the same time, allow us to enlarge the training dataset and to produce hallucinations from randomly combined attribute pairs at ease. Experiments on widely used datasets demonstrate that the proposed approach yields results superior to the state of the art.
Published in: IEEE Transactions on Cybernetics ( Volume: 52 , Issue: 1 , January 2022 )
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Image generation is crucial for many tasks like the computer aided and aesthetic design. Recent research has demonstrated that realistic and visually plausible images can be generated from various sources, such as captions, dialogues, and scene graphs [1]–[3], among which caption-based generation, the dual task of captioning [4], [5], has attracted much attention due to its simple input format and hence the potentially very wide application domains.
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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