46 citations · 151 across the 40 of their papers we have counts for
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Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Jijie Wu, Dongliang Chang, Aneeshan Sain +5
The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labe…
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…
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches
Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Subhadeep Koley +4
The human visual system is remarkable in learning new visual concepts from just a few examples. This is precisely the goal behind few-shot class incremental learning (FSCIL), where…
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…