The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Semi-supervised training is a means for reducing the effort needed to prepare the training set by training the model with a small number of fully labeled examples and ...

In this paper, we introduce several concepts of tightness for a sequence of random variables taking values in the space of normal and upper-semicontinuous fuzzy sets with compact support in Rp and give some characterizations of their concepts. Also, counter-examples for the relationships between the concepts of tightness are given.

In this paper, the notion of βS∗−compactness is introduced in L−fuzzy topological spaces based on S∗−compactness. A βS∗−compactness L-fuzzy set is S∗−compactness and also β−compactness. Some of its properties are discussed. We give some characterizations of βS∗−compactness in terms of pre-open, regular open and semi-open L−fuzzy set. It is proved that βS∗−compactness is a good extension of β−co...

AbstractOpen Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely normal videos are applicable any testing but suffer a high false positive rate. In contrast, weakly supervised methods effective detecting could fail an open world. We develop method for the OpenVAD pro...

In open set recognition (OSR), almost all existing methods are designed specially for recognizing individual instances, even these instances collectively coming in batch. Recognizers decision either reject or categorize them to some known class using empirically-set threshold. Thus the threshold plays a key role. However, selection it usually depends on knowledge of classes, inevitably incurrin...

Many existing conditional Generative Adversarial Networks (cGANs) are limited to conditioning on pre-defined and fixed class-level semantic labels or attributes. We propose an open set GAN architecture (OpenGAN) that is conditioned per-input sample with a feature embedding drawn from metric space. Using state-of-the-art learning model encodes both fine-grained information, we able generate samp...

Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data update existing models. Open-set recognition, however, requires ability recognize examples and reject new/unknown There are two main challenges in this matter. First, class discovery: algorithm needs not only but it must ...