Abstract—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.
 
Keywords—residual neural networks, binary neural networks, coordinate attention, threshold tuning, edge AI, model compression


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