# FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context

Source: [FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context](<https://www.sota2.com/research/paper/68fa6d1a1e209131cea37c0a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:25:36.384Z

Authors: Pinaki Nath Chowdhury, Aneeshan Sain, Ayan Kumar Bhunia, Tao Xiang, Yulia Gryaditskaya, Yi-Zhe Song

Published: 2022-03-04T03:00:51.000Z

## Abstract

We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey scene content well but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises 10,000 freehand scene vector sketches with per point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description. Using our dataset, we study for the first time the problem of fine-grained image retrieval from freehand scene sketches and sketch captions. We draw insights on: (i) Scene salience encoded in sketches using the strokes temporal order; (ii) Performance comparison of image retrieval from a scene sketch and an image caption; (iii) Complementarity of information in sketches and image captions, as well as the potential benefit of combining the two modalities. In addition, we extend a popular vector sketch LSTM-based encoder to handle sketches with larger complexity than was supported by previous work. Namely, we propose a hierarchical sketch decoder, which we leverage at a sketch-specific "pre-text" task. Our dataset enables for the first time research on freehand scene sketch understanding and its practical applications.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.02113>)

[PDF](<https://arxiv.org/pdf/2203.02113.pdf>)