53 citations · 312 across the 79 of their papers we have counts for
13 papers · 1 filter
DreamColour: Controllable Video Colour Editing without Training
Chaitat Utintu, Pinaki Nath Chowdhury, Aneeshan Sain +3
Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with tempo…
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…
SketchDeco: Training-Free Latent Composition for Precise Sketch Colourisation
Chaitat Utintu, Yi-Zhe Song
We introduce SketchDeco, a training-free approach to sketch colourisation that bridges the gap between professional design needs and intuitive, region-based control. Our method emp…
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…
How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
In this paper, we propose a novel abstraction-aware sketch-based image retrieval framework capable of handling sketch abstraction at varied levels. Prior works had mainly focused o…
What Sketch Explainability Really Means for Downstream Tasks
Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia +3
In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond…
SketchINR: A First Look into Sketches as Implicit Neural Representations
Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury +4
We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimen…
IPR-NeRF: Ownership Verification meets Neural Radiance Field
Win Kent Ong, Kam Woh Ng, Chee Seng Chan +2
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressi…