This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-s...
详细信息
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-s...
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.
Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a chall...
详细信息
The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range o...
详细信息
A few adaptive algorithms for generalized eigen-decomposition have been proposed, which are very useful in many applications such as digital mobile communications, blind signal separation, etc. These algorithms are al...
详细信息
A few adaptive algorithms for generalized eigen-decomposition have been proposed, which are very useful in many applications such as digital mobile communications, blind signal separation, etc. These algorithms are all focusing on extracting principal generalized eigenvectors. However, in many practical applications such as dimension reduction and signal processing, extracting the minor generalized eigenvectors adaptively are needed. Because of little literatures in the community, we discuss several approaches that lead to a few novel algorithms for extracting minor generalized eigenvectors. First, we derive an adaptive algorithms by using a single-layer linear forward neural network from the viewpoint of linear discriminant analysis (LDA). And the algorithm to extract multiple minor generalized eigenvectors are also proposed by using orthogonality property. Second, by using gradient ascent approach of some objective functions, we can derive more algorithms and explain the first algorithm. Then, we extend these algorithms to minor generalized eigenvector problem. Theoretical analysis shows that these algorithms are stable and convergent to the minor generalized eigenvectors. Simulations have been conducted for illustration of the efficiency and effectiveness of our algorithms.
The goal of Web page categorization is to classify Web documents into a certain number of predefined categories. Previous works in this area employed a large number of labeled training documents for supervised learnin...
详细信息
暂无评论