415 citations · 1.1k across the 56 of their papers we have counts for
63 papers
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley +3
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailab…
Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch
Aneeshan Sain, Subhajit Maity, Pinaki Nath Chowdhury +3
As sketch research has collectively matured over time, its adaptation for at-mass commercialisation emerges on the immediate horizon. Despite an already mature research endeavour f…
SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models
Subhadeep Koley, Tapas Kumar Dutta, Aneeshan Sain +3
While foundation models have revolutionised computer vision, their effectiveness for sketch understanding remains limited by the unique challenges of abstract, sparse visual inputs…
SketchYourSeg: Mask-Free Subjective Image Segmentation via Freehand Sketches
Subhadeep Koley, Viswanatha Reddy Gajjala, Aneeshan Sain +4
We introduce SketchYourSeg, a novel framework that establishes freehand sketches as a powerful query modality for subjective image segmentation across entire galleries through a si…
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…
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…
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…
DemoCaricature: Democratising Caricature Generation with a Rough Sketch
Dar-Yen Chen, Ayan Kumar Bhunia, Subhadeep Koley +3
In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch. Our objective…
Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes
Hmrishav Bandyopadhyay, Subhadeep Koley, Ayan Das +5
In this paper, we democratise 3D content creation, enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills. We introduce…
Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings
Ayan Kumar Bhunia, Subhadeep Koley, Amandeep Kumar +4
Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sket…
Picture that Sketch: Photorealistic Image Generation from Abstract Sketches
Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain +3
Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image - just like those shown in Fig. 1(a), all non-…
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not
Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury +3
In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalis…
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley +4
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is…
Data-Free Sketch-Based Image Retrieval
Abhra Chaudhuri, Ayan Kumar Bhunia, Yi-Zhe Song +1
Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning (DFL). For the first time, we identify that for dat…
What Can Human Sketches Do for Object Detection?
Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain +3
Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited…
Towards Practicality of Sketch-Based Visual Understanding
Ayan Kumar Bhunia
Sketches have been used to conceptualise and depict visual objects from pre-historic times. Sketch research has flourished in the past decade, particularly with the proliferation o…
Adaptive Fine-Grained Sketch-Based Image Retrieval
Ayan Kumar Bhunia, Aneeshan Sain, Parth Shah +4
The recent focus on Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) has shifted towards generalising a model to new categories without any training data from them. In real-worl…