mlsanityData to 14 Oct 2026

Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval

arXiv 2407.01810 ↗First posted 1 Jul 2024cs.CVcited by 2

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Abstract (arXiv)

In this paper, we delve into the intricate dynamics of Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) by addressing a critical yet overlooked aspect -- the choice of viewpoint during sketch creation. Unlike photo systems that seamlessly handle diverse views through extensive datasets, sketch systems, with limited data collected from fixed perspectives, face challenges. Our pilot study, employing a pre-trained FG-SBIR model, highlights the system's struggle when query-sketches differ in viewpoint from target instances. Interestingly, a questionnaire however shows users desire autonomy, with a significant percentage favouring view-specific retrieval. To reconcile this, we advocate for a view-aware system, seamlessly accommodating both view-agnostic and view-specific tasks. Overcoming dataset limitations, our first contribution leverages multi-view 2D projections of 3D objects, instilling cross-modal view awareness. The second contribution introduces a customisable cross-modal feature through disentanglement, allowing effortless mode switching. Extensive experiments on standard datasets validate the effectiveness of our method.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 47 of its 77 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

47 of 77 references.

References · 47

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35 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animationscited by 8
  2. 2026SEA: Evaluating Sketch Abstraction Efficiency via Element-level Commonsense Visual Question Answeringcited by 0