[Paper] On the Temporality for Sketch Representation Learning

Source: arXiv - 2512.04007v1

Overview

The paper On the Temporality for Sketch Representation Learning dives into a surprisingly under‑explored question: does the order in which a sketch is drawn matter for modern deep‑learning models? By systematically testing different ways of feeding the stroke‑by‑stroke data to neural networks, the authors show that temporal information can be leveraged—but only when it’s encoded correctly. Their findings tighten the bridge between human‑centric sketching behavior and machine‑friendly representations, a gap that matters for any product that consumes hand‑drawn input (e.g., note‑taking apps, design tools, or AI‑assisted illustration).

Key Contributions

• Empirical study of temporal encodings – compares absolute vs. relative coordinate encodings and classic positional embeddings for sketch sequences.
• Decoder architecture comparison – demonstrates that non‑autoregressive decoders consistently beat autoregressive ones on downstream tasks.
• Task‑dependent temporality analysis – shows that the benefit of preserving stroke order varies across tasks such as classification, retrieval, and generation.
• Guidelines for practitioners – provides concrete recommendations on when and how to treat sketches as sequences in real‑world pipelines.

Methodology

1. Dataset & Pre‑processing – The authors use publicly available sketch datasets (e.g., QuickDraw) that contain raw stroke data: a series of (x, y) points plus pen‑up/pen‑down flags.
2. Temporal Encodings
   • Absolute coordinates: each point is fed with its raw (x, y) values.
   • Relative coordinates: each point is expressed as a delta from the previous point.
   • Positional encodings: sinusoidal embeddings (as in Transformers) added on top of the coordinates.
3. Model Variants
   • Encoder: a shared Vision Transformer‑style encoder that ingests the sequence of points.
   • Decoders:
     • Autoregressive (predict next stroke point conditioned on previous ones).
     • Non‑autoregressive (predict the whole stroke set in parallel).
4. Evaluation Tasks
   • Sketch classification (recognizing the object category).
   • Sketch retrieval (finding similar sketches in a gallery).
   • Sketch generation (re‑creating a sketch from a latent code).
5. Metrics – standard accuracy for classification, mean average precision for retrieval, and Fréchet Sketch Distance for generation quality.

Results & Findings

• Absolute coordinates win across the board, confirming that raw stroke positions preserve more discriminative information than deltas.
• Non‑autoregressive decoders consistently outperform autoregressive ones, likely because they avoid error propagation and can exploit the full sketch context simultaneously.
• Temporal importance is task‑specific: classification benefits most from absolute ordering, while retrieval shows a smaller gap, and generation quality is relatively robust to the choice of encoding.

Practical Implications

• Designing sketch‑aware UI components – When building a drawing canvas that feeds data to a backend model (e.g., for auto‑tagging), store and transmit the raw (x, y) points rather than compressing them into deltas.
• Choosing model architecture – For latency‑critical applications (e.g., real‑time sketch search), a non‑autoregressive decoder can deliver faster inference without sacrificing accuracy.
• Data augmentation pipelines – Since relative encodings underperform, augmentations that preserve absolute geometry (e.g., scaling, rotation) are safer than those that heavily perturb point‑to‑point deltas.
• Cross‑modal retrieval – Systems that match sketches to photos or 3D models can prioritize absolute‑coordinate encodings to boost retrieval precision.
• Edge deployment – The study suggests that a relatively lightweight Transformer encoder + parallel decoder can achieve state‑of‑the‑art results, making it feasible to run on‑device (smartphones, tablets) for offline sketch analysis.

Limitations & Future Work

• Dataset bias – Experiments rely on large, crowd‑sourced sketch corpora that may not reflect professional or domain‑specific drawing styles (e.g., architectural sketches).
• Temporal granularity – The study treats each recorded point as a timestep; finer‑grained timing information (stroke speed, pressure) was not explored.
• Model scalability – While non‑autoregressive decoders are faster, they still require a full Transformer stack; future work could investigate lightweight convolutional or graph‑based alternatives.
• Multi‑modal extensions – Integrating sketch temporality with accompanying text or voice commands remains an open avenue for richer human‑computer interaction.

Bottom line: If you’re building any system that consumes hand‑drawn input, treat sketches as absolute‑coordinate sequences and favor parallel (non‑autoregressive) decoders. This simple shift can unlock measurable gains in accuracy and speed, bringing AI‑powered sketch understanding closer to production‑ready reality.

Authors

• Marcelo Isaias de Moraes Junior
• Moacir Antonelli Ponti

Paper Information

• arXiv ID: 2512.04007v1
• Categories: cs.CV, cs.AI
• Published: December 3, 2025
• PDF: Download PDF