Search

With the widespread adoption of object detection models across various industries, the interpretability of these detectors has become an important research topic. The interpretability of a detector helps humans understand which areas significantly contribute to the model's decision. Furthermore, the interpretability enhances the credibility of detectors and helps identify their strengths and weaknesses. Due to the ability to provide intuitive explanations of models, the saliency map has been widely employed in the field of interpreting deep models. Model-agnostic interpretability methods are more general approaches as they treat the model as a black box without considering its internal complexity structure. However, existing model-agnostic interpretability methods often introduce "noise" into saliency maps by applying random masking and fixed masking granularity. This noise reduces the quality and interpretability of the generated saliency maps. To address this challenge and obtain more interpretable saliency maps for object detection models, this paper proposes a model-agnostic progressive saliency map generation method based on a hierarchical framework called MAPSM. In MAPSM, an adaptive masking partition mechanism is introduced to adapt the masking granularity to different object sizes. Additionally, MAPSM employs a saliency-driven mask generation strategy to effectively reduce the "noise". Utilizing a hierarchical framework, MAPSM progressively discovers and refines the saliency areas of objects, resulting in more interpretable saliency maps. To evaluate the quality of the saliency maps generated by MAPSM, we compare it with other methods in multiple metrics. Experimental results demonstrate that our method produces saliency maps with better quality and interpretability. • A progressive saliency map generation method for explaining object detectors. • The saliency map visualizes the contribution of individual pixels to a prediction. • The quality of the saliency map is validated by multiple metrics. • The method is usable to detectors without accessing their internal information. [ABSTRACT FROM AUTHOR]

Slickwater hydraulic fracturing technology is widely used in shale reservoirs to form complex networks and improve hydrocarbon recovery, but the proppant cannot be delivered to the remote fractures due to its low viscosity. Pickering emulsion stabilized by nanoparticles is developed as a hydraulic fracturing fluid for shale reservoirs. In this study, nano-silicas were used to prepare and stabilize Pickering emulsion. During fracturing process, this emulsion is broken to release nano-silicas, which would adsorb on fractures surfaces or assemble in fractures. The nano-silicas in low mass concentration Pickering emulsion pave on the surfaces of shales uniformly, hence stabilize the shale and prevent clay hydration. And when in high mass concentration Pickering emulsion, nano-silicas can accumulate in fractures to support and prevent them from closing. Since Pickering emulsion itself contains nanoparticles to serve as proppants, it can penetrate the entire fracture and complex network which maximize the effective fracture area and stimulate reservoir volume, furthermore replacing traditional proppant pumping. [ABSTRACT FROM AUTHOR]