Salient Object Detection via Bounding-box Supervision

The success of fully supervised saliency detection models depends on a large number of pixel-wise labeling. In this paper, we work on bounding-box based weakly-supervised saliency detection to relieve the labeling effort. Given the bounding box annotation, we observe that pixels inside the bounding box may contain extensive labeling noise. However, as a large amount of background is excluded, the foreground bounding box region contains a less complex background, making it possible to perform handcrafted features-based saliency detection with only the cropped foreground region. As the conventional handcrafted features are not representative enough, leading to noisy saliency maps, we further introduce structure-aware self-supervised loss to regularize the structure of the prediction. Further, we claim that pixels outside the bounding box should be background, thus partial cross-entropy loss function can be used to accurately localize the accurate background region. Experimental results on six benchmark RGB saliency datasets illustrate the effectiveness of our model.

A Survey on Semi-parametric Machine Learning Technique for Time Series Forecasting

Artificial Intelligence (AI) has recently shown its capabilities for almost every field of life. Machine Learning, which is a subset of AI, is a `HOT' topic for researchers. Machine Learning outperforms other classical forecasting techniques in almost all-natural applications. It is a crucial part of modern research. As per this statement, Modern Machine Learning algorithms are hungry for big data. Due to the small datasets, the researchers may not prefer to use Machine Learning algorithms. To tackle this issue, the main purpose of this survey is to illustrate, demonstrate related studies for significance of a semi-parametric Machine Learning framework called Grey Machine Learning (GML). This kind of framework is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting. In this paper, a primer survey on the GML framework is provided for researchers. To allow an in-depth understanding for the readers, a brief description of Machine Learning, as well as various forms of conventional grey forecasting models are discussed. Moreover, a brief description on the importance of GML framework is presented.

Revisiting the Loss Weight Adjustment in Object Detection

By definition, object detection requires a multi-task loss in order to solve classification and regression tasks simultaneously. However, loss weight tends to be set manually in actuality. Therefore, a very practical problem that has not been studied so far arises: how to quickly find the loss weight that fits the current loss functions. In addition, when we choose different regression loss functions, whether the loss weight need to be adjusted and if so, how should it be adjusted still is a problem demanding prompt solution. In this paper, through experiments and theoretical analysis of prediction box shifting, we firstly find out three important conclusions about optimal loss weight allocation strategy, including (1) the classification loss curve decays faster than regression loss curve; (2) loss weight is less than 1; (3) the gap between classification and regression loss weight should not be too large. Then, based on the above conclusions, we propose an Adaptive Loss Weight Adjustment(ALWA) to solve the above two problems by dynamically adjusting the loss weight in the training process, according to statistical characteristics of loss values. By incorporating ALWA into both one-stage and two-stage object detectors, we show a consistent improvement on their performance using L1, SmoothL1 and CIoU loss, performance measures on popular object detection benchmarks including PASCAL VOC and MS COCO. The code is available at https://github.com/ywx-hub/ALWA.