# Goal-Driven Sequential Data Abstraction

arXiv: https://arxiv.org/abs/1907.12336 · HTML: https://mlsanity.com/paper/1907.12336/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval](https://mlsanity.com/paper/2103.13990/) (2021)
- [Sketch3T: Test-Time Training for Zero-Shot SBIR](https://mlsanity.com/paper/2203.14691/) (2022)
- [Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval](https://mlsanity.com/paper/2203.14817/) (2022)
- [Abstracting Sketches through Simple Primitives](https://mlsanity.com/paper/2207.13543/) (2022)
- [ChiroDiff: Modelling chirographic data with Diffusion Models](https://mlsanity.com/paper/2304.03785/) (2023)
- [How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?](https://mlsanity.com/paper/2403.07203/) (2024)
- [SketchGPT: Autoregressive Modeling for Sketch Generation and Recognition](https://mlsanity.com/paper/2405.03099/) (2024)
- [Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching](https://mlsanity.com/paper/2602.12280/) (2026)
- [Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval](https://mlsanity.com/paper/2407.01810/) (2024)
- [Do Generalised Classifiers really work on Human Drawn Sketches?](https://mlsanity.com/paper/2407.03893/) (2024)
- [Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch](https://mlsanity.com/paper/2505.23763/) (2025)

## Abstract (arXiv)

Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction. In the latter this capability is exploited for saving space or human time by summarizing the essence of input data. In this paper we study a general reinforcement learning based framework for learning to abstract sequential data in a goal-driven way. The ability to define different abstraction goals uniquely allows different aspects of the input data to be preserved according to the ultimate purpose of the abstraction. Our reinforcement learning objective does not require human-defined examples of ideal abstraction. Importantly our model processes the input sequence holistically without being constrained by the original input order. Our framework is also domain agnostic -- we demonstrate applications to sketch, video and text data and achieve promising results in all domains.