Empirical Study on Fusion Strategy in RGB-T Salient Object Detection

In the research field of RGB-Thermal saliency object detection (RGB-T SOD), the effective exploitation of the complementary characteristics of the two modalities represents a major challenge for enhancing detection performance. Current fusion methodologies can be roughly classified into early fusion and middle fusion strategies, with prevalent techniques primarily encompassing concatenation, summation, and multiplication of the two modalities. To in depth assess the efficacy of these fusion strategies, we took an empirical investigation on them. Our findings demonstrate that the concatenation of middle features constitutes a more advantageous fusion strategy, yielding superior performance and demonstrating enhanced stability. Furthermore, observing the unique properties of thermal (T) images, we introduced gamma correction as a novel data augmentation methodology to RGB-T SOD. We subsequently evaluated the responses across varying correction parameter ranges, revealing that while the response to this data augmentation technique differs across various models, data augmentation is found to be effective in general. Building upon these findings, we proposed the Gamma Correction Network (GaCNet). Specifically, we also integrated image pyramid mechanism in a lightweight manner, which facilitates a more effective recovery of fine-grained image details. Significant improvement was achieved on commonly used RGB-T testing datasets, especially in VT821 dataset, manifesting the effectiveness of our method.