Auto-Parsing Network for Image Captioning and Visual Question Answering

• URL: http://arxiv.org/abs/2108.10568v1
• Date: Tue, 24 Aug 2021 08:14:35 GMT
• Title: Auto-Parsing Network for Image Captioning and Visual Question Answering
• Authors: Xu Yang and Chongyang Gao and Hanwang Zhang and Jianfei Cai
• Abstract summary: We propose an Auto-Parsing Network (APN) to discover and exploit the input data's hidden tree structures. Specifically, we impose a Probabilistic Graphical Model (PGM) parameterized by the attention operations on each self-attention layer to incorporate sparse assumption.
• Score: 101.77688388554097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an Auto-Parsing Network (APN) to discover and exploit the input data's hidden tree structures for improving the effectiveness of the Transformer-based vision-language systems. Specifically, we impose a Probabilistic Graphical Model (PGM) parameterized by the attention operations on each self-attention layer to incorporate sparse assumption. We use this PGM to softly segment an input sequence into a few clusters where each cluster can be treated as the parent of the inside entities. By stacking these PGM constrained self-attention layers, the clusters in a lower layer compose into a new sequence, and the PGM in a higher layer will further segment this sequence. Iteratively, a sparse tree can be implicitly parsed, and this tree's hierarchical knowledge is incorporated into the transformed embeddings, which can be used for solving the target vision-language tasks. Specifically, we showcase that our APN can strengthen Transformer based networks in two major vision-language tasks: Captioning and Visual Question Answering. Also, a PGM probability-based parsing algorithm is developed by which we can discover what the hidden structure of input is during the inference.

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