219 citations · 282 across the 33 of their papers we have counts for
33 papers
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…
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…
Move Anything with Layered Scene Diffusion
Jiawei Ren, Mengmeng Xu, Jui-Chieh Wu +3
Diffusion models generate images with an unprecedented level of quality, but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spati…
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…
Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
This paper, for the first time, explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text…