BITS: Bit-Extendable Incremental Hashing in Open Environments

Hashing is an effective technique for large-scale image retrieval. However, traditional hashing models typically follow a closed-set assumption, which fails to satisfy the practicality of real-world tasks. In this paper, we explore a meaningful yet overlooked question: is there a hashing paradigm that not only supports rehearsal-free online incremental coding for single-pass data streams but also adapts to potentially expanding concept spaces in open environments? Instead of presetting fixed bit lengths, we suggest adjusting the bit length dynamically based on the number of encountered categories, meanwhile enabling bit extension of existing hash codes to match the adaptive code lengths without knowledge forgetting. Therefore, we propose a Bit-extendable IncremenTal haShing (BITS) method for image retrieval in open environments. Specifically, we identify a blurry incremental setup to better simulate realistic scenarios, revisiting the widely-used data-incremental and class-incremental settings. With this challenging setup, a three-phase framework is designed to efficiently perform incremental hashing, which jointly solves online continual coding and bit extension with adaptive code lengths. Through the well-designed hashing paradigm, BITS achieves comparable performance to offline hashing methods while significantly saving computational resources. Comprehensive experiments on six benchmarks demonstrate the superiority of our BITS in dynamic scenarios. The source code is available at https://github.com/yxinwang/BITS.