Adaptively Clustering Neighbor Elements for Image-Text Generation

• URL: http://arxiv.org/abs/2301.01955v3
• Date: Mon, 24 Jun 2024 04:45:10 GMT
• Title: Adaptively Clustering Neighbor Elements for Image-Text Generation
• Authors: Zihua Wang, Xu Yang, Hanwang Zhang, Haiyang Xu, Ming Yan, Fei Huang, Yu Zhang,
• Abstract summary: We propose a novel Transformer-based image-to-text generation model termed as textbfACF. ACF adaptively clusters vision patches into object regions and language words into phrases to implicitly learn object-phrase alignments. Experiment results demonstrate the effectiveness of ACF, which outperforms most SOTA captioning and VQA models.
• Score: 78.82346492527425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel Transformer-based image-to-text generation model termed as \textbf{ACF} that adaptively clusters vision patches into object regions and language words into phrases to implicitly learn object-phrase alignments for better visual-text coherence. To achieve this, we design a novel self-attention layer that applies self-attention over the elements in a local cluster window instead of the whole sequence. The window size is softly decided by a clustering matrix that is calculated by the current input data and thus this process is adaptive. By stacking these revised self-attention layers to construct ACF, the small clusters in the lower layers can be grouped into a bigger cluster, \eg vision/language. ACF clusters small objects/phrases into bigger ones. In this gradual clustering process, a parsing tree is generated which embeds the hierarchical knowledge of the input sequence. As a result, by using ACF to build the vision encoder and language decoder, the hierarchical object-phrase alignments are embedded and then transferred from vision to language domains in two popular image-to-text tasks: Image captioning and Visual Question Answering. The experiment results demonstrate the effectiveness of ACF, which outperforms most SOTA captioning and VQA models and achieves comparable scores compared with some large-scale pre-trained models. Our code is available \href{https://github.com/ZihuaEvan/ACFModel/}{[here]}.

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