24 citations · 64 across the 18 of their papers we have counts for
29 papers
PartCraft: Crafting Creative Objects by Parts
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
This paper propels creative control in generative visual AI by allowing users to "select". Departing from traditional text or sketch-based methods, we for the first time allow user…
Do Generalised Classifiers really work on Human Drawn Sketches?
Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Aneeshan Sain +4
This paper, for the first time, marries large foundation models with human sketch understanding. We demonstrate what this brings -- a paradigm shift in terms of generalised sketch…
Freeview Sketching: View-Aware Fine-Grained Sketch-Based Image Retrieval
Aneeshan Sain, Pinaki Nath Chowdhury, Subhadeep Koley +2
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…
ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery
Kam Woh Ng, Xiatian Zhu, Yi-Zhe Song +1
Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitat…
It's All About Your Sketch: Democratising Sketch Control in Diffusion Models
Subhadeep Koley, Ayan Kumar Bhunia, Deeptanshu Sekhri +4
This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the proce…
You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
Two primary input modalities prevail in image retrieval: sketch and text. While text is widely used for inter-category retrieval tasks, sketches have been established as the sole p…