CVMMSep 15, 2023

Beyond Domain Gap: Exploiting Subjectivity in Sketch-Based Person Retrieval

arXiv:2309.08372v121 citationsh-index: 59Has Code
Originality Incremental advance
AI Analysis

This work addresses the challenge of retrieving persons using subjective sketches for security applications, but it is incremental as it builds on existing sketch re-ID methods by focusing on subjectivity.

The paper tackles the problem of sketch-based person re-identification by addressing subjectivity in witness sketches, introducing a large-scale dataset with over 4,763 sketches and 32,668 photos, and achieving leading performance on new benchmarks.

Person re-identification (re-ID) requires densely distributed cameras. In practice, the person of interest may not be captured by cameras and, therefore, needs to be retrieved using subjective information (e.g., sketches from witnesses). Previous research defines this case using the sketch as sketch re-identification (Sketch re-ID) and focuses on eliminating the domain gap. Actually, subjectivity is another significant challenge. We model and investigate it by posing a new dataset with multi-witness descriptions. It features two aspects. 1) Large-scale. It contains over 4,763 sketches and 32,668 photos, making it the largest Sketch re-ID dataset. 2) Multi-perspective and multi-style. Our dataset offers multiple sketches for each identity. Witnesses' subjective cognition provides multiple perspectives on the same individual, while different artists' drawing styles provide variation in sketch styles. We further have two novel designs to alleviate the challenge of subjectivity. 1) Fusing subjectivity. We propose a non-local (NL) fusion module that gathers sketches from different witnesses for the same identity. 2) Introducing objectivity. An AttrAlign module utilizes attributes as an implicit mask to align cross-domain features. To push forward the advance of Sketch re-ID, we set three benchmarks (large-scale, multi-style, cross-style). Extensive experiments demonstrate our leading performance in these benchmarks. Dataset and Codes are publicly available at: https://github.com/Lin-Kayla/subjectivity-sketch-reid

Code Implementations1 repo
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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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