TIF: Threshold Interception and Fusion for Compact and Fine-Grained Visual Attribution

The blackbox nature of deep models has prompted a growing interest in explaining their inner workings and decision-making processes. Although backpropagation (BP)-based attribution methods are popular for visual interpretation, existing methods frequently yield implausible outcomes. For example, gradient-based attribution methods tend to highlight irrelevant regions and generate noise, while CAM-based attributions suffer from low resolution and blurry results. These limitations undermine their ability to correctly identify the target objects and fail to provide the desired justification to guarantee the credibility of the model's decision-making. In this article, we analyze plausibility issues in the frequency domain and point out that the plausibility issues correspond to frequency-domain incompleteness, i.e., the frequency-domain representation of explanations lacks low- or high-frequency components. Then, we propose a straightforward yet effective approach, threshold interception and fusion (TIF), to address this issue by fusing multilayer attributions. Our strategy involves collecting attribution results for all neurons and dividing the attribution map into a concept region that represents the current neuron and background regions based on a given threshold value
$\alpha$
. We then fuse these concept regions with the neuron weights in each layer and upsample the layer attributions to match the input size. Finally, we obtain the overall attribution by summing the layer attributions pixelwise. Our experiments demonstrate TIF efficacy by consistently enhancing visual performance across a variety of gradient-based attributions. To further demonstrate the ability to provide compact and fine-grained target objects, we directly employ TIF for the weakly supervised semantic segmentation task. Our results illustrate that TIF significantly outperforms existing methods without additional supervision or architectural modifications. We also observe an overall TIF improvement in the fidelity metric, suggesting that compactness and fine-graininess are not only plausibility issues but also fidelity issues.