Knowledge driven Description Synthesis for Floor Plan Interpretation
Shreya Goyal, Chiranjoy Chattopadhyay, Gaurav Bhatnagar

TL;DR
This paper introduces two deep learning models, DSIC and TBDG, for generating detailed textual descriptions from floor plan images, improving flexibility and robustness over existing methods.
Contribution
It presents novel models that leverage visual features and paragraph-based understanding for more accurate and flexible floor plan image captioning.
Findings
TBDG outperforms state-of-the-art methods in experiments.
Models effectively extract and utilize visual and textual features.
Proposed methods enhance description robustness and detail.
Abstract
Image captioning is a widely known problem in the area of AI. Caption generation from floor plan images has applications in indoor path planning, real estate, and providing architectural solutions. Several methods have been explored in literature for generating captions or semi-structured descriptions from floor plan images. Since only the caption is insufficient to capture fine-grained details, researchers also proposed descriptive paragraphs from images. However, these descriptions have a rigid structure and lack flexibility, making it difficult to use them in real-time scenarios. This paper offers two models, Description Synthesis from Image Cue (DSIC) and Transformer Based Description Generation (TBDG), for the floor plan image to text generation to fill the gaps in existing methods. These two models take advantage of modern deep neural networks for visual feature extraction and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsLinear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Layer Normalization · Adam · Byte Pair Encoding · Attention Is All You Need · Label Smoothing · Dropout · Residual Connection
