RGB-D salient object detection (SOD) recently has attracted increasing research interest by benefiting conventional RGB SOD with extra depth information. However, existing RGB-D SOD models often fail to perform well in terms of both efficiency and accuracy, which hinders their potential applications on mobile devices and real-world problems. An underlying challenge is that the model accuracy usually degrades when the model is simplified to have few parameters. To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy. DQFM resorts to the alignment of low-level RGB and depth features, as well as holistic attention of the depth stream to explicitly control and enhance cross-modal fusion. We embed DQFM to obtain an efficient light-weight model called DFM-Net, where we also design a tailored depth backbone and a two-stage decoder for further efficiency consideration. Extensive experimental results demonstrate that our DFM-Net achieves state-of-the-art accuracy when comparing to existing non-efficient models, and meanwhile runs at 140ms on CPU (2.2× faster than the prior fastest efficient model) with only ∼8.5Mb model size (14.9% of the prior lightest). Our code will be available at https://github.com/zwbx/DFM-Net.
CCS CONCEPTS• Computing methodologies → Interest point and salient region detections.
Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control.
Coal roadway driving is an important part of the underground mining system, and very common in Chinese coal mines. However, the high concentration of respirable dust produced in the blasting operation poses a great hazard to miners’ health as well as the underground environment. In this paper, based on the direct simulation Monte Carlo method, the gas–solid two-phase flow model of particle movement is established to study the respirable dust distribution in blasting driving face. The results show that there is an obvious vortex region in which airflow velocity is lower than that close to the roadway wall and driving face. After blasting, respirable dust in the front of the dust group jet from the driving face cannot be discharged timely, with the result that its concentration is higher than the critical value until it is expelled from the roadway, whereas respirable dust concentration at the back of the dust group is gradually diluted and exhibits an alternate thin dense phase distribution. Meanwhile, respirable dust concentration in the breathing zone is relatively higher than that at the top and bottom of roadway. The accuracy of numerical simulation results is verified by field measurements. The research results are helpful for further understanding the evolution of respirable dust distribution after blasting, and are good for providing guidance for efficient controlling of respirable dust and improving the working environment for underground miners.
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