Information-Bottleneck Driven Binary Neural Network for Change Detection
This work addresses the challenge of efficient change detection for applications like remote sensing by developing a specialized binary neural network, representing an incremental improvement over existing binarization methods.
The paper tackles the problem of low accuracy in binary neural networks for change detection by proposing BiCD, which introduces an Information Bottleneck-based auxiliary objective to enhance representational power and feature separability, achieving state-of-the-art performance on street-view and remote sensing datasets.
In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent input data and distinguish between changed and unchanged regions. This results in significantly lower detection accuracy compared to real-valued networks. To overcome these challenges, BiCD enhances both the representational power and feature separability of BNNs, improving detection performance. Specifically, we introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input information while promoting better feature discrimination. Since directly computing mutual information under the IB principle is intractable, we design a compact, learnable auxiliary module as an approximation target, leading to a simple yet effective optimization strategy that minimizes both reconstruction loss and standard change detection loss. Extensive experiments on street-view and remote sensing datasets demonstrate that BiCD establishes a new benchmark for BNN-based change detection, achieving state-of-the-art performance in this domain.