More Photos are All You Need: Semi-Supervised Learning for Fine-Grained
Sketch Based Image Retrieval
- URL: http://arxiv.org/abs/2103.13990v1
- Date: Thu, 25 Mar 2021 17:27:08 GMT
- Title: More Photos are All You Need: Semi-Supervised Learning for Fine-Grained
Sketch Based Image Retrieval
- Authors: Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Yongxin Yang,
Tao Xiang, Yi-Zhe Song
- Abstract summary: We introduce a novel semi-supervised framework for cross-modal retrieval.
At the centre of our design is a sequential photo-to-sketch generation model.
We also introduce a discriminator guided mechanism to guide against unfaithful generation.
- Score: 112.1756171062067
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A fundamental challenge faced by existing Fine-Grained Sketch-Based Image
Retrieval (FG-SBIR) models is the data scarcity -- model performances are
largely bottlenecked by the lack of sketch-photo pairs. Whilst the number of
photos can be easily scaled, each corresponding sketch still needs to be
individually produced. In this paper, we aim to mitigate such an upper-bound on
sketch data, and study whether unlabelled photos alone (of which they are many)
can be cultivated for performances gain. In particular, we introduce a novel
semi-supervised framework for cross-modal retrieval that can additionally
leverage large-scale unlabelled photos to account for data scarcity. At the
centre of our semi-supervision design is a sequential photo-to-sketch
generation model that aims to generate paired sketches for unlabelled photos.
Importantly, we further introduce a discriminator guided mechanism to guide
against unfaithful generation, together with a distillation loss based
regularizer to provide tolerance against noisy training samples. Last but not
least, we treat generation and retrieval as two conjugate problems, where a
joint learning procedure is devised for each module to mutually benefit from
each other. Extensive experiments show that our semi-supervised model yields
significant performance boost over the state-of-the-art supervised
alternatives, as well as existing methods that can exploit unlabelled photos
for FG-SBIR.
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