Semi-Supervised Sketch Based Image Retrieval
1 papers with code • 0 benchmarks • 0 datasets
Whilst the number of photos can be easily scaled, each corresponding sketch still needs to be individually produced for fine-grained sketch-based image retrieval. The objective is to mitigate such an upper-bound on sketch data, and study whether unlabelled photos alone (of which they are many) can be cultivated for performance gain.
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Sketch Based Image Retrieval
Most implemented papers
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
AyanKumarBhunia/semisupervised-FGSBIR
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CVPR 2021
A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs.
