• RUSHIKESH KALE
• SOURAB MARPHALE
• YASH OZA

In this project, we look into the dilemma of zeroshot sketch-based image retrieval (ZS-SBIR), which involves using human sketches as queries to retrieve images from previously unseen categories. We make a significant contribution to prior art by proposing a ZS-SBIR scenario that represents a major advancement in its practical implementation. To exploit the domain void, highly abstract amateur human sketches are intentionally sourced rather than those found in existing datasets, which are mostly semi-photorealistic. The ZS-SBIR system is then used to model sketches and images into a single embedding space. To alleviate the domain gap, a novel strategy for mutual knowledge between domains has been created. To aid semantic transition, external semantic awareness is further embedded. Surprisingly, retrieval efficiency outperforms on existing datasets with a simplified version of our model. On the newly proposed dataset, we compare our complete model to a variety of alternatives and show that it outperforms them.

Convolutional Neural Network, Deep Learning, Image Retrieval, Sketch Based Image Retrieval, zero shot.

"SKETCH BASED IMAGE RETRIEVAL (SBIR)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.80-83, April-2021, Available :http://www.jetir.org/papers/JETIR2104211.pdf

"SKETCH BASED IMAGE RETRIEVAL (SBIR)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp80-83, April-2021, Available at : http://www.jetir.org/papers/JETIR2104211.pdf