PanDA: Panoptic Data Augmentation

The recently proposed panoptic segmentation task presents a significant challenge of image understanding with computer vision by unifying semantic segmentation and instance segmentation tasks. In this paper we present an efficient and novel panoptic data augmentation (PanDA) method which operates exclusively in pixel space, requires no additional data or training, and is computationally cheap to implement. By retraining original state-of-the-art models on PanDA augmented datasets generated with a single frozen set of parameters, we show robust performance gains in panoptic segmentation, instance segmentation, as well as detection across models, backbones, dataset domains, and scales. Finally, the effectiveness of unrealistic-looking training images synthesized by PanDA suggest that one should rethink the need for image realism for efficient data augmentation

A fast algorithm for All-Pairs-Shortest-Paths suitable for neural networks

Given a directed graph of nodes and edges connecting them, a common problem is to find the shortest path between any two nodes. Here I show that the shortest path distances can be found by a simple matrix inversion: If the edges are given by the adjacency matrix $A_{ij}$ then with a suitably small value of $\gamma$ the shortest path distances are $$D_{ij} = \operatorname{ceil} \left( {\frac{\log {\left[ {\left({\mathbf{1}}-\gamma {\mathbf{A}}\right)^{-1}} \right]}_{ij}}{\log \gamma}} \right)$$ I derive some bounds on $\gamma$ useful for a practical application. Even when the distance function is not globally accurate across the entire graph, it still works locally to instruct pursuit of the shortest path. In this mode, it also extends to weighted graphs with positive edge weights. For a wide range of dense graphs this distance function is computationally faster than the best available alternative. Finally I show that this method leads naturally to a neural network solution of the all-pairs-shortest-path problem.Comment: 11 pages, 4 figures, see also https://github.com/markusmeister/APS

Can Humans Really Discriminate 1 Trillion Odors?

A recent paper in a prominent science magazine claims to show that humans can discriminate at least 1 trillion odors. The authors reached that conclusion after performing just 260 comparisons of two smells, of which about half could be discriminated. Furthermore the paper claims that the human ability to discriminate smells vastly exceeds our abilities to discriminate colors or musical tones. Here I show that all these statements are wrong by astronomical factors. A reanalysis of the authors' experiments shows they are also consistent with humans discriminating just 10 odors. The paper's extravagant claims are based on errors of mathematical logic. Further analysis highlights the importance of establishing how many dimensions the perceptual odor space has. I review some arguments on the topic and propose experimental avenues towards an answer.Comment: 14 pages, 4 figures. Revised version has same technical content, more introduction for non-experts, more thoughts in the discussio

Rapid Innate Defensive Responses of Mice to Looming Visual Stimuli

Much of brain science is concerned with understanding the neural circuits that underlie specific behaviors. While the mouse has become a favorite experimental subject, the behaviors of this species are still poorly explored. For example, the mouse retina, like that of other mammals, contains ∼20 different circuits that compute distinct features of the visual scene [1 and 2]. By comparison, only a handful of innate visual behaviors are known in this species—the pupil reflex [3], phototaxis [4], the optomotor response [5], and the cliff response [6]—two of which are simple reflexes that require little visual processing. We explored the behavior of mice under a visual display that simulates an approaching object, which causes defensive reactions in some other species [7 and 8]. We show that mice respond to this stimulus either by initiating escape within a second or by freezing for an extended period. The probability of these defensive behaviors is strongly dependent on the parameters of the visual stimulus. Directed experiments identify candidate retinal circuits underlying the behavior and lead the way into detailed study of these neural pathways. This response is a new addition to the repertoire of innate defensive behaviors in the mouse that allows the detection and avoidance of aerial predators

Divergence of visual channels in the inner retina

Bipolar cells form parallel channels that carry visual signals from the outer to the inner retina. Each type of bipolar cell is thought to carry a distinct visual message to select types of amacrine cells and ganglion cells. However, the number of ganglion cell types exceeds that of the bipolar cells providing their input, suggesting that bipolar cell signals diversify on transmission to ganglion cells. We explored in the salamander retina how signals from individual bipolar cells feed into multiple ganglion cells and found that each bipolar cell was able to evoke distinct responses among ganglion cells, differing in kinetics, adaptation and rectification properties. This signal divergence resulted primarily from interactions with amacrine cells that allowed each bipolar cell to send distinct signals to its target ganglion cells. Our findings indicate that individual bipolar cell–ganglion cell connections have distinct transfer functions. This expands the number of visual channels in the inner retina and enhances the computational power and feature selectivity of early visual processing

Rats maintain a binocular field centered on the horizon

In this letter, we attempt to correct a potentially serious misperception arising from the paper “Rats maintain an overhead binocular field at the expense of constant fusion”. While the authors repeatedly emphasize that the animal’s binocular field is overhead, the authors’ own data show that the truth is quite different, even orthogonal: the binocular field is in fact centered dead-ahead in front of the animal, tapering to a sliver both above and below the animal. We predict that this paper will be widely cited for something that it does not demonstrate, a concern that is borne out by the paper’s earliest citation

The Projective Field of Retinal Bipolar Cells and Its Modulation by Visual Context

The receptive field of a sensory neuron spells out all the receptor inputs it receives. To understand a neuron’s role in the circuit, one also needs to know its projective field, namely the outputs it sends to all downstream cells. Here we present the projective fields of the primary excitatory neurons in a sensory circuit. We stimulated single bipolar cells of the salamander retina and recorded simultaneously from a population of ganglion cells. Individual bipolar cell signals diverge through polysynaptic pathways into ganglion cells of many different types and over surprisingly large distance. However, the strength and polarity of the projection depend on the cell types involved. Furthermore, visual stimulation strongly modulates the bipolar cell projective field, in opposite direction for different cell types. In this way, the context from distant parts of the visual field can control the routing of signals in the inner retina

Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data

A central goal in neurobiology is to relate the expression of genes to the structural and functional properties of neuronal types, collectively called their phenotypes. Single-cell RNA sequencing can measure the expression of thousands of genes in thousands of neurons. How to interpret the data in the context of neuronal phenotypes? We propose a supervised learning approach that factorizes the gene expression data into components corresponding to individual phenotypic characteristics and their interactions. This new method, which we call factorized linear discriminant analysis (FLDA), seeks a linear transformation of gene expressions that varies highly with only one phenotypic factor and minimally with the others. We further leverage our approach with a sparsity-based regularization algorithm, which selects a few genes important to a specific phenotypic feature or feature combination. We applied this approach to a single-cell RNA-Seq dataset of Drosophila T4/T5 neurons, focusing on their dendritic and axonal phenotypes. The analysis confirms results obtained by conventional methods but also points to new genes related to the phenotypes and an intriguing hierarchy in the genetic organization of these cells