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Fine-Grained VR Sketching: Dataset and Insights
Ling Luo, Yulia Gryaditskaya, Yongxin Yang +2
We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the ske…
Towards 3D VR-Sketch to 3D Shape Retrieval
Ling Luo, Yulia Gryaditskaya, Yongxin Yang +2
Growing free online 3D shapes collections dictated research on 3D retrieval. Active debate has however been had on (i) what the best input modality is to trigger retrieval, and (ii…
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…
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…
Dynamic Instance Domain Adaptation
Zhongying Deng, Kaiyang Zhou, Da Li +3
Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's training samples come with domain labels (e.g., painting, photo). Samples from each domain a…
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…