Pixelwise Instance Segmentation with a Dynamically Instantiated Network

Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level. We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label. Most approaches adapt object detectors to produce segments instead of boxes. In contrast, our method is based on an initial semantic segmentation module, which feeds into an instance subnetwork. This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances. This part of our model is dynamically instantiated to produce a variable number of instances per image. Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals. Therefore, unlike some related work, a pixel cannot belong to multiple instances. Furthermore, far more precise segmentations are achieved, as shown by our state-of-the-art results (particularly at high IoU thresholds) on the Pascal VOC and Cityscapes datasets.Comment: CVPR 201

Money in Gas-Like Markets: Gibbs and Pareto Laws

We consider the ideal-gas models of trading markets, where each agent is identified with a gas molecule and each trading as an elastic or money-conserving (two-body) collision. Unlike in the ideal gas, we introduce saving propensity $\lambda$ of agents, such that each agent saves a fraction $\lambda$ of its money and trades with the rest. We show the steady-state money or wealth distribution in a market is Gibbs-like for $\lambda=0$, has got a non-vanishing most-probable value for $\lambda \ne 0$ and Pareto-like when $\lambda$ is widely distributed among the agents. We compare these results with observations on wealth distributions of various countries.Comment: 4 pages, 2 eps figures, in Conference Procedings of International Conference on "Unconventional Applications of Statistical Physics", Kolkata, India, March 2003; paper published in Physica Scripta T106 (2003) 3

Productivity, Preferences and UIP deviations in an Open Economy Business Cycle Model

We show that a flex-price two-sector open economy DSGE model can explain the poor degree of international risk sharing and exchange rate disconnect. We use a suite of model evaluation measures and examine the role of (i) traded and non-traded sectors; (ii) financial market incompleteness; (iii) preference shocks; (iv) deviations from UIP condition for the exchange rates; and (v) creditor status in net foreign assets. We find that there is a good case for both traded and non-traded productivity shocks as well as UIP deviations in explaining the puzzles

Example of a first-order N\'eel to Valence-Bond-Solid transition in two-dimensions

We consider the $S=1/2$ Heisenberg model with nearest-neighbor interaction $J$ and an additional multi-spin interaction $Q_3$ on the square lattice. The $Q_3$ term consists of three bond-singlet projectors and is chosen to favor the formation of a valence-bond solid (VBS) where the valence bonds (singlet pairs) form a staggered pattern. The model exhibits a quantum phase transition from the N\'eel state to the VBS as a function of $Q_3/J$. We study the model using quantum Monte Carlo (stochastic series expansion) simulations. The N\'eel-VBS transition in this case is strongly first-order in nature, in contrast to similar previously studied models with continuous transitions into columnar VBS states. The qualitatively different transitions illustrate the important role of an emerging U(1) symmetry in the latter case, which is not possible in the present model due to the staggered VBS pattern (which does not allow local fluctuations necessary to rotate the local VBS order parameter).Comment: 8 pages, 7 figure

Superfluid Insulator Transitions of Hard-Core Bosons on the Checkerboard Lattice

We study hard-core bosons on the checkerboard lattice with nearest neighbour unfrustrated hopping $t$ and `tetrahedral' plaquette charging energy $U$. Analytical arguments and Quantum Monte Carlo simulations lead us to the conclusion that the system undergoes a zero temperature ($T$) quantum phase transition from a superfluid phase at small $U/t$ to a large $U/t$ Mott insulator phase with $\rho$ = 1/4 for a range of values of the chemical potential $\mu$. Further, the quarter-filled insulator breaks lattice translation symmetry in a characteristic four-fold ordering pattern, and occupies a lobe of finite extent in the $\mu$-$U/t$ phase diagram. A Quantum Monte-Carlo study slightly away from the tip of the lobe provides evidence for a direct weakly first-order superfluid-insulator transition away from the tip of the lobe. While analytical arguments leads us to conclude that the transition {\em at} the tip of the lobe belongs to a different landau-forbidden second-order universality class, an extrapolation of our numerical results suggests that the size of the first-order jump does not go to zero even at the tip of the lobe.Comment: published versio

Metrics with Galilean Conformal Isometry

The Galilean Conformal Algebra (GCA) arises in taking the non-relativistic limit of the symmetries of a relativistic Conformal Field Theory in any dimensions. It is known to be infinite-dimensional in all spacetime dimensions. In particular, the 2d GCA emerges out of a scaling limit of linear combinations of two copies of the Virasoro algebra. In this paper, we find metrics in dimensions greater than two which realize the finite 2d GCA (the global part of the infinite algebra) as their isometry by systematically looking at a construction in terms of cosets of this finite algebra. We list all possible sub-algebras consistent with some physical considerations motivated by earlier work in this direction and construct all possible higher dimensional non-degenerate metrics. We briefly study the properties of the metrics obtained. In the standard one higher dimensional "holographic" setting, we find that the only non-degenerate metric is Minkowskian. In four and five dimensions, we find families of non-trivial metrics with a rather exotic signature. A curious feature of these metrics is that all but one of them are Ricci-scalar flat.Comment: 20 page

Holistic, Instance-Level Human Parsing

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201