Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of feature vectors (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space, whereas negative bags only contain negative instances. The classes in a MIL problem are therefore not treated in the...

Instance search is an interesting task as well a challenging issue due to the lack of effective feature representation. In this paper, instance level representation built upon fully convolutional instance-aware segmentation proposed. The ROI-pooled from segmented region. So that instances in various sizes and layouts are represented by deep features uniform length. This further enhanced use def...

In order to function in unstructured environments, robots need the ability recognize unseen objects. We take a step this direction by tackling problem of segmenting object instances tabletop environments. However, type large-scale real-world dataset required for task typically does not exist most robotic settings, which motivates use synthetic data. Our proposed method, instance segmentation (U...

Instance segmentation has gained attention in various computer vision fields, such as autonomous driving, drone control, and sports analysis. Recently, many successful models have been developed, which can be classified into two categories: accuracy- speed-focused. Accuracy inference time are important for real-time applications of this task. However, these just present measured on different ha...

With a laboratory experiment, we study the impact of buy-options and the corresponding buy-price on revenues and bidding behavior in (online) proxy-auctions with independent private valuations. We show that temporary buy-options may reduce revenues for two reasons: At low buy-prices, the application of the buy-option avoids revenue-enhancing bidding; at high buy-prices, bidders are reluctant to...