Threshold-Tuning Coordinate Attention for Binarized Neural Networks
—Binary Neural Networks (BNNs) are highly attractive for mobile and embedded vision due to their extremely low memory footprint and efficient bit-level convolutions. However, binarization often causes severe information loss and weakens the effectiveness of conventional attention modules designed for full-precision networks. We propose a BNN-oriented attention mechanism, Threshold-Tuning Coordinate Attention (TT-CA), which applies attention by adjusting the binarization decision boundary rather than performing fine-grained multiplicative reweighting. Built upon Coordinate Attention (CA), TT-CA generates a spatially aware threshold ( τ ) from coordinate-wise pooled features and applies a subtractive threshold to induce controllable sign flips, thereby recovering discriminative capability. To balance modulation capacity and deployment efficiency, we quantize the HardSigmoid gating outputs into K-bit discrete levels, enabling lightweight and hardware-friendly gating while avoiding the overly coarse behavior of 1-bit gates. We further explore CA-derived design variants under low-bit settings, including simplified normalization and lightweight bottlenecks, and integrate TT-CA into a compact ResNet14-Wide backbone. Experiments on CIFAR-100 and Tiny ImageNet show consistent accuracy gains over binarized baselines and standard attention plug-ins with modest overhead, reducing Top-1 error by 2.45% on Tiny ImageNet and 0.94% on CIFAR-100. Ablation studies on the threshold strength ( δ ) validate the effectiveness and robustness of threshold tuning for efficient BNNs.