Visual recognition of objects : behavioral, computational, and neurobiological aspects

I surveyed work on visual object recognition and perception. In animals, vision has been studied mainly on the behavioral and neurobiological levels. Behavioral data typically show what the visual system, by itself or together with the rest of the organism, is capable of. They show, for example, that humans can recognie objects regardless of size and position, but that rotated objects pose problems. Important insights into the organization of behavior have also been provided by people who suffered localized brain damage. We have learned that the brain is divided into areas subserving different and relatively well-defined behaviors. The visual system itself is also organized in different subsystems; the visual cortex alone contains nearly twenty maps of the visual field. And individual neurons respond selectively to visual stimuli, e.g., the orientation of line segments, color, direction of motion, and, most intriguingly, faces. The question is how the actions of all these neurons produce the behavior we observe. How do neurons represent the shape of objects such that they can be recognized? Before we can answer the question, we have to understand the computational aspect of shape representation, the nature of the problem as it were. Many methods for representing shape have been explored, mainly by computer scientists, but so far no satisfactory answers have been found

Cortical Dynamics of Language

The human capability for fluent speech profoundly directs inter-personal communication and, by extension, self-expression. Language is lost in millions of people each year due to trauma, stroke, neurodegeneration, and neoplasms with devastating impact to social interaction and quality of life. The following investigations were designed to elucidate the neurobiological foundation of speech production, building towards a universal cognitive model of language in the brain. Understanding the dynamical mechanisms supporting cortical network behavior will significantly advance the understanding of how both focal and disconnection injuries yield neurological deficits, informing the development of therapeutic approaches

Blind Image Decomposition

We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step toward real-world vision systems. To facilitate research on this new task, we construct multiple benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN.Comment: ECCV 2022. Project page: https://junlinhan.github.io/projects/BID.html. Code: https://github.com/JunlinHan/BI

Representational Kinds

Many debates in philosophy focus on whether folk or scientific psychological notions pick out cognitive natural kinds. Examples include memory, emotions and concepts. A potentially interesting type of kind is: kinds of mental representations (as opposed, for example, to kinds of psychological faculties). In this chapter we outline a proposal for a theory of representational kinds in cognitive science. We argue that the explanatory role of representational kinds in scientific theories, in conjunction with a mainstream approach to explanation in cognitive science, suggest that representational kinds are multi-level. This is to say that representational kinds’ properties cluster at different levels of explanation and allow for intra- and inter-level projections

Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework

The evolution of grounded spatial language

This book presents groundbreaking robotic experiments on how and why spatial language evolves. It provides detailed explanations of the origins of spatial conceptualization strategies, spatial categories, landmark systems and spatial grammar by tracing the interplay of environmental conditions, communicative and cognitive pressures. The experiments discussed in this book go far beyond previous approaches in grounded language evolution. For the first time, agents can evolve not only particular lexical systems but also evolve complex conceptualization strategies underlying the emergence of category systems and compositional semantics. Moreover, many issues in cognitive science, ranging from perception and conceptualization to language processing, had to be dealt with to instantiate these experiments, so that this book contributes not only to the study of language evolution but to the investigation of the cognitive bases of spatial language as well

The evolution of grounded spatial language

This book presents groundbreaking robotic experiments on how and why spatial language evolves. It provides detailed explanations of the origins of spatial conceptualization strategies, spatial categories, landmark systems and spatial grammar by tracing the interplay of environmental conditions, communicative and cognitive pressures. The experiments discussed in this book go far beyond previous approaches in grounded language evolution. For the first time, agents can evolve not only particular lexical systems but also evolve complex conceptualization strategies underlying the emergence of category systems and compositional semantics. Moreover, many issues in cognitive science, ranging from perception and conceptualization to language processing, had to be dealt with to instantiate these experiments, so that this book contributes not only to the study of language evolution but to the investigation of the cognitive bases of spatial language as well