Multimodal Attention Branch Network for Perspective-Free Sentence Generation
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Abstract (arXiv)
In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as "bring me the yellow toy from the upper part of the white shelf" includes referring expressions, i.e., "from the white upper part of the white shelf". To solve this task, we propose a multimodal attention branch network (Multi-ABN) which generates natural sentences in an end-to-end manner. Multi-ABN uses multiple images of the same fixed scene to generate sentences that are not tied to a particular viewpoint. This approach combines a linguistic attention branch mechanism with several attention branch mechanisms. We evaluated our approach, which outperforms the state-of-the-art method on a standard metrics. Our method also allows us to visualize the alignment between the linguistic input and the visual features.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Sep 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1.
4 papers.
- 2024Polos: Multimodal Metric Learning from Human Feedback for Image Captioningcited by 46
- 2023JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Modelscited by 6
- 2021Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructionscited by 4
- 2021Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulationscited by 2
- 2024Polos: Multimodal Metric Learning from Human Feedback for Image Captioning28 Feb 2024
- 2023JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models7 Nov 2023
- 2021Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions2 Jul 2021
- 2021Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations12 Feb 2021