Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Zero-shot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on this type of paired data. Current zero-shot methods mainly focus on using pre-trained discriminative models (e.g., CLIP). However, we have observed that generative models (e.g., Stable Diffusion) have potentially understood the relationships between various visual elements and text descriptions, which are rarely investigated in this task. In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models. We demonstrate that without a proposal generator, a generative model alone can achieve comparable performance to existing SOTA weakly-supervised models. When we combine both generative and discriminative models, our Ref-Diff outperforms these competing methods by a significant margin. This indicates that generative models are also beneficial for this task and can complement discriminative models for better referring segmentation. Our code is publicly available at https://github.com/kodenii/Ref-Diff

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting

In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error)

Deterioration of Mechanical Properties of Axial Compression Concrete Columns with Corroded Stirrups Coupling on Load and Chloride

To research the deterioration of the mechanical properties of stirrup-corroded concrete columns under the effect of load and chloride, accelerated corrosion and load carrying capacity tests were carried out on concrete columns subjected to long-term axial loading by means of dry and wet cycles with extra electric currents. The test results showed that under the effect of axial load and chloride, the corrosion-induced cracks of stirrup-corroded concrete columns mainly developed along the direction of the longitudinal reinforcing steel bars (cracks along longitudinal reinforcing steel bars caused by corrosion) and there were almost no corrosion-induced cracks along the direction of the corroded stirrups. The length and maximum width of the corrosion-induced cracks increased with the stirrup corrosion rate, but the average width of the corrosion-induced cracks did not change significantly. After the stirrup-corroded column reached the ultimate load, the concrete cover spalled off in pieces along the corrosion-induced cracks and loading cracks, the core concrete was crushed, and the test column produced obvious brittle failure. With the increase in the corrosion rate of stirrups, the stiffness and ultimate bearing capacity of the column decreased. Considering factors such as damage to the column section caused by stirrup corrosion, the decrease in the lateral restraint effect of the corroded stirrup on the longitudinal reinforcing steel bars, and buckling of the longitudinal reinforcing steel bars, the ultimate bearing capacity prediction model of the short column subjected to axial compression due to stirrup corrosion was established. The calculated values of the model were in good agreement with the measured values, indicating the model has good applicability