تقرير
Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval
| العنوان: | Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval |
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| المؤلفون: | Sain, Aneeshan, Chowdhury, Pinaki Nath, Koley, Subhadeep, Bhunia, Ayan Kumar, Song, Yi-Zhe |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Accepted in European Conference on Computer Vision (ECCV) 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.01810 |
| رقم الأكسشن: | edsarx.2407.01810 |
| قاعدة البيانات: | arXiv |
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