11 citations · 71 across the 35 of their papers we have counts for
6 papers · 2 filters
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…
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text
Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain +3
In this paper, we extend scene understanding to include that of human sketch. The result is a complete trilogy of scene representation from three diverse and complementary modaliti…
Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval
Ayan Kumar Bhunia, Subhadeep Koley, Abdullah Faiz Ur Rahman Khilji +4
Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal for its widespread adopti…
Partially Does It: Towards Scene-Level FG-SBIR with Partial Input
Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Viswanatha Reddy Gajjala +3
We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial". A quick pilot study reveals: (i) a scene…
Sketch3T: Test-Time Training for Zero-Shot SBIR
Aneeshan Sain, Ayan Kumar Bhunia, Vaishnav Potlapalli +3
Zero-shot sketch-based image retrieval typically asks for a trained model to be applied as is to unseen categories. In this paper, we question to argue that this setup by definitio…
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context
Pinaki Nath Chowdhury, Aneeshan Sain, Ayan Kumar Bhunia +3
We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey scene content…