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Existing person re-identification (re-id) works mostly consider short-termapplication scenarios without clothes change. In real-world, however, we oftendress differently across space and time. To solve this contrast, a few recentattempts have been made on long-term re-id with clothes change. Currently, oneof the most significant limitations in this field is the lack of a largerealistic benchmark. In this work, we contribute a large, realistic long-termperson re-identification benchmark, named as DeepChange. It has several uniquecharacteristics: (1) Realistic and rich personal appearance (e.g., clothes andhair style) and variations: Highly diverse clothes change and styles, withvarying reappearing gaps in time from minutes to seasons, different weatherconditions (e.g., sunny, cloudy, windy, rainy, snowy, extremely cold) andevents (e.g., working, leisure, daily activities). (2) Rich camera setups: Rawvideos were recorded by 17 outdoor varying resolution cameras operating in areal-world surveillance system. (3) The currently largest number of (17)cameras, (1, 121) identities, and (178, 407) bounding boxes, over the longesttime span (12 months). Further, we investigate multimodal fusion strategies fortackling the clothes change challenge. Extensive experiments show that ourfusion models outperform a wide variety of state-of-the-art models onDeepChange. Our dataset and documents are available athttps://github.com/PengBoXiangShang/deepchange.

DeepChange: A Large Long-Term Person Re-Identification Benchmark with Clothes Change