Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Spatial representation and visual impairement - Developmental trends and new technological tools for assessment and rehabilitation

It is well known that perception is mediated by the five sensory modalities (sight, hearing, touch, smell and taste), which allows us to explore the world and build a coherent spatio-temporal representation of the surrounding environment. Typically, our brain collects and integrates coherent information from all the senses to build a reliable spatial representation of the world. In this sense, perception emerges from the individual activity of distinct sensory modalities, operating as separate modules, but rather from multisensory integration processes. The interaction occurs whenever inputs from the senses are coherent in time and space (Eimer, 2004). Therefore, spatial perception emerges from the contribution of unisensory and multisensory information, with a predominant role of visual information for space processing during the first years of life. Despite a growing body of research indicates that visual experience is essential to develop spatial abilities, to date very little is known about the mechanisms underpinning spatial development when the visual input is impoverished (low vision) or missing (blindness). The thesis's main aim is to increase knowledge about the impact of visual deprivation on spatial development and consolidation and to evaluate the effects of novel technological systems to quantitatively improve perceptual and cognitive spatial abilities in case of visual impairments. Chapter 1 summarizes the main research findings related to the role of vision and multisensory experience on spatial development. Overall, such findings indicate that visual experience facilitates the acquisition of allocentric spatial capabilities, namely perceiving space according to a perspective different from our body. Therefore, it might be stated that the sense of sight allows a more comprehensive representation of spatial information since it is based on environmental landmarks that are independent of body perspective. Chapter 2 presents original studies carried out by me as a Ph.D. student to investigate the developmental mechanisms underpinning spatial development and compare the spatial performance of individuals with affected and typical visual experience, respectively visually impaired and sighted. Overall, these studies suggest that vision facilitates the spatial representation of the environment by conveying the most reliable spatial reference, i.e., allocentric coordinates. However, when visual feedback is permanently or temporarily absent, as in the case of congenital blindness or blindfolded individuals, respectively, compensatory mechanisms might support the refinement of haptic and auditory spatial coding abilities. The studies presented in this chapter will validate novel experimental paradigms to assess the role of haptic and auditory experience on spatial representation based on external (i.e., allocentric) frames of reference. Chapter 3 describes the validation process of new technological systems based on unisensory and multisensory stimulation, designed to rehabilitate spatial capabilities in case of visual impairment. Overall, the technological validation of new devices will provide the opportunity to develop an interactive platform to rehabilitate spatial impairments following visual deprivation. Finally, Chapter 4 summarizes the findings reported in the previous Chapters, focusing the attention on the consequences of visual impairment on the developmental of unisensory and multisensory spatial experience in visually impaired children and adults compared to sighted peers. It also wants to highlight the potential role of novel experimental tools to validate the use to assess spatial competencies in response to unisensory and multisensory events and train residual sensory modalities under a multisensory rehabilitation

Robot navigation in vineyards based on the visual vanish point concept

One of the biggest challenges of autonomous navigation in robots for agriculture is the path following in a large dimension map and various terrains. An important ability is to follow corridors and or vine rows which are frequent situation and with some complexity given the outline of real vegetation. One method to locate and guide the robot in between vineyards is making use of vanishing point detection on vine rows in order to obtain a reference point and send the adequate velocity commands to the motors. This detection will be conceived utilizing convectional image processing algorithms and Deep Learning techniques. It will be necessary to adapt the image processing algorithms or Deep Learning for use in ROS 2 context.One of the biggest challenges of autonomous navigation in robots for agriculture is the path following in a large dimension map and various terrains. An important ability is to follow corridors and or vine rows which are frequent situation and with some complexity given the outline of real vegetation. One method to locate and guide the robot in between vineyards is making use of vanishing point detection on vine rows in order to obtain a reference point and send the adequate velocity commands to the motors. This detection will be conceived utilizing convectional image processing algorithms and Deep Learning techniques. It will be necessary to adapt the image processing algorithms or Deep Learning for use in ROS 2 context

Neurofly 2008 abstracts : the 12th European Drosophila neurobiology conference 6-10 September 2008 Wuerzburg, Germany

This volume consists of a collection of conference abstracts