Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells

We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA laye

NewsPaperBox – Online News Space: a visual model for representing the social space of a website

NewsPaperBox propounds an alternative visual model utilizing the treemap algorithm to represent the collective use of a website that evolves in response to user interaction. While the technology currently exists to track various user behaviors such as number of clicks, duration of stay on a given web site, these statistics are not yet employed to influence the visual representation of that site’s design in real time. In that sense, this project propounds an alternative modeling of a representational outlook of a website that is developed by collaborations and competitions of its global users. This paper proposes the experience of cyberspace as a generative process driven by its effective user participation

Economics for the Masses : The Visual Display of Economic Knoledge in the United Staes (1921-1945)

The rise of visual representation in economics textbooks after WWII is one of the main features of contemporary economics. In this paper, we argue that this development has been preceded by a no less significant rise of visual representation in the larger literature devoted to social and scientific issues, including economic textbooks for non-economists as well as newspapers and magazines. During the interwar era, editors, propagandists and social scientists altogether encouraged the use of visual language as the main vehicle to spread information and opinions about the economy to a larger audience. These new ways of visualizing social facts, which most notably helped shape the understanding of economic issues by various audiences during the years of the Great Depression, were also conceived by their inventors as alternative ways of practicing economics: in opposition to the abstraction of “neoclassical” economics, these authors wanted to use visual representation as a way to emphasize the human character of the discipline and did not accept the strict distinction between the creation and the diffusion of economic knowledge. We explore different yet related aspects of these developments by studying the use of visual language in economics textbooks intended for non-specialists, in periodicals such as the Survey, a monthly magazine intended for an audience of social workers, the Americanization of Otto Neurath's pictorial statistics and finally the use of those visual representations by various state departments and administrations under Roosevelt's legislature (including the much-commented Historical Section of the Farm Security Administration). We show how visualizations that have been created in opposition to neoclassical economics have lost most of their theoretical content when used widely for policy purposes while being simultaneously integrated into the larger American culture. It is our claim that those issues, which are familiar to those involved in cultural and visual studies, are also of crucial importance to apprehend the later developments of modern economics.Visualization, economocs, American Economy, Otto Neurath, Rexford Tugwell, Roosevelt, Roy Stryker, Photographs, Pictorial Statistics

Continued fractions for permutation statistics

We explore a bijection between permutations and colored Motzkin paths that has been used in different forms by Foata and Zeilberger, Biane, and Corteel. By giving a visual representation of this bijection in terms of so-called cycle diagrams, we find simple translations of some statistics on permutations (and subsets of permutations) into statistics on colored Motzkin paths, which are amenable to the use of continued fractions. We obtain new enumeration formulas for subsets of permutations with respect to fixed points, excedances, double excedances, cycles, and inversions. In particular, we prove that cyclic permutations whose excedances are increasing are counted by the Bell numbers.Comment: final version formatted for DMTC

Generalized Max Pooling

State-of-the-art patch-based image representations involve a pooling operation that aggregates statistics computed from local descriptors. Standard pooling operations include sum- and max-pooling. Sum-pooling lacks discriminability because the resulting representation is strongly influenced by frequent yet often uninformative descriptors, but only weakly influenced by rare yet potentially highly-informative ones. Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions. We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector -- hence the name Generalized Max Pooling (GMP). It involves equalizing the similarity between each patch and the pooled representation, which is shown to be equivalent to re-weighting the per-patch statistics. We show on five public image classification benchmarks that the proposed GMP can lead to significant performance gains with respect to heuristic alternatives.Comment: (to appear) CVPR 2014 - IEEE Conference on Computer Vision & Pattern Recognition (2014

On color image quality assessment using natural image statistics

Color distortion can introduce a significant damage in visual quality perception, however, most of existing reduced-reference quality measures are designed for grayscale images. In this paper, we consider a basic extension of well-known image-statistics based quality assessment measures to color images. In order to evaluate the impact of color information on the measures efficiency, two color spaces are investigated: RGB and CIELAB. Results of an extensive evaluation using TID 2013 benchmark demonstrates that significant improvement can be achieved for a great number of distortion type when the CIELAB color representation is used

Visual Representations: Defining Properties and Deep Approximations

Visual representations are defined in terms of minimal sufficient statistics of visual data, for a class of tasks, that are also invariant to nuisance variability. Minimal sufficiency guarantees that we can store a representation in lieu of raw data with smallest complexity and no performance loss on the task at hand. Invariance guarantees that the statistic is constant with respect to uninformative transformations of the data. We derive analytical expressions for such representations and show they are related to feature descriptors commonly used in computer vision, as well as to convolutional neural networks. This link highlights the assumptions and approximations tacitly assumed by these methods and explains empirical practices such as clamping, pooling and joint normalization.Comment: UCLA CSD TR140023, Nov. 12, 2014, revised April 13, 2015, November 13, 2015, February 28, 201

Redundancy effects in the processing of emotional faces

AbstractHow does the visual system represent the ensemble statistics of visual objects? This question has received intense interest in vision research, yet most studies have focused on the extraction of mean statistics rather than its dispersion. This study focuses on another aspect of ensemble statistics: the redundancy of the sample. In two experiments, participants were faster judging the facial expression and gender of multiple faces than a single face. The redundancy gain was equivalent for multiple identical faces and for multiple faces of different identities. To test whether the redundancy gain was due to increased strength in perceptual representation, we measured the magnitude of facial expression aftereffects. The aftereffects were equivalent when induced by a single face and by four identical faces, ruling out increased perceptual strength as an explanation for the redundancy gain. We conclude that redundant faces facilitate perception by enhancing the robustness of representation of each face

Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.Comment: CVPR 201