pwc-archive/papers-with-abstracts huggingface dataset:
Human Mesh Recovery from Multiple Shots
Authors: Georgios Pavlakos, Jitendra Malik, Angjoo Kanazawa
Date: 2020-12-17 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Reconstruction, Human Mesh Recovery

Videos from edited media like movies are a useful, yet under-explored source of information. The rich variety of appearance and interactions between humans depicted over a large temporal context in these films could be a valuable source of data. However, the richness of data comes at the expense of fundamental challenges such as abrupt shot changes and close up shots of actors with heavy truncation, which limits the applicability of existing human 3D understanding methods. In this paper, we address these limitations with an insight that while shot changes of the same scene incur a discontinuity between frames, the 3D structure of the scene still changes smoothly. This allows us to handle frames before and after the shot change as multi-view signal that provide strong cues to recover the 3D state of the actors. We propose a multi-shot optimization framework, which leads to improved 3D reconstruction and mining of long sequences with pseudo ground truth 3D human mesh. We show that the resulting data is beneficial in the training of various human mesh recovery models: for single image, we achieve improved robustness; for video we propose a pure transformer-based temporal encoder, which can naturally handle missing observations due to shot changes in the input frames. We demonstrate the importance of the insight and proposed models through extensive experiments. The tools we develop open the door to processing and analyzing in 3D content from a large library of edited media, which could be helpful for many downstream applications. Project page: https://geopavlakos.github.io/multishot
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
Authors: Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, Angjoo Kanazawa
Date: 2020-12-17 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Generation, Perpetual View Generation, Video Generation

We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image. This is a challenging problem that goes far beyond the capabilities of current view synthesis methods, which quickly degenerate when presented with large camera motions. Methods for video generation also have limited ability to produce long sequences and are often agnostic to scene geometry. We take a hybrid approach that integrates both geometry and image synthesis in an iterative `\emph{render}, \emph{refine} and \emph{repeat}' framework, allowing for long-range generation that cover large distances after hundreds of frames. Our approach can be trained from a set of monocular video sequences. We propose a dataset of aerial footage of coastal scenes, and compare our method with recent view synthesis and conditional video generation baselines, showing that it can generate plausible scenes for much longer time horizons over large camera trajectories compared to existing methods. Project page at https://infinite-nature.github.io/.
Reconstructing Hand-Object Interactions in the Wild
Authors: Zhe Cao, Ilija Radosavovic, Angjoo Kanazawa, Jitendra Malik
Date: 2020-12-17 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Reconstruction, Object

In this work we explore reconstructing hand-object interactions in the wild. The core challenge of this problem is the lack of appropriate 3D labeled data. To overcome this issue, we propose an optimization-based procedure which does not require direct 3D supervision. The general strategy we adopt is to exploit all available related data (2D bounding boxes, 2D hand keypoints, 2D instance masks, 3D object models, 3D in-the-lab MoCap) to provide constraints for the 3D reconstruction. Rather than optimizing the hand and object individually, we optimize them jointly which allows us to impose additional constraints based on hand-object contact, collision, and occlusion. Our method produces compelling reconstructions on the challenging in-the-wild data from the EPIC Kitchens and the 100 Days of Hands datasets, across a range of object categories. Quantitatively, we demonstrate that our approach compares favorably to existing approaches in the lab settings where ground truth 3D annotations are available.
Reduction in the complexity of 1D 1H-NMR spectra by the use of Frequency to Information Transformation
Authors: Homayoun Valafar, Faramarz Valafar
Date: 2020-12-16 00:00:00

Analysis of 1H-NMR spectra is often hindered by large variations that occur during the collection of these spectra. Large solvent and standard peaks, base line drift and negative peaks (due to improper phasing) are among some of these variations. Furthermore, some instrument dependent alterations, such as incorrect shimming, are also embedded in the recorded spectrum. The unpredictable nature of these alterations of the signal has rendered the automated and instrument independent computer analysis of these spectra unreliable. In this paper, a novel method of extracting the information content of a signal (in this paper, frequency domain 1H-NMR spectrum), called the frequency-information transformation (FIT), is presented and compared to a previously used method (SPUTNIK). FIT can successfully extract the relevant information to a pattern matching task present in a signal, while discarding the remainder of a signal by transforming a Fourier transformed signal into an information spectrum (IS). This technique exhibits the ability of decreasing the inter-class correlation coefficients while increasing the intra-class correlation coefficients. Different spectra of the same molecule, in other words, will resemble more to each other while the spectra of different molecules will look more different from each other. This feature allows easier automated identification and analysis of molecules based on their spectral signatures using computer algorithms.
StarcNet: Machine Learning for Star Cluster Identification
Authors: Gustavo Perez, Matteo Messa, Daniela Calzetti, Subhransu Maji, Dooseok Jung, Angela Adamo, Mattia Siressi
Date: 2020-12-16 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic Ultraviolet Survey). StarcNet (STAR Cluster classification NETwork) is a multi-scale convolutional neural network (CNN) which achieves an accuracy of 68.6% (4 classes)/86.0% (2 classes: cluster/non-cluster) for star cluster classification in the images of the LEGUS galaxies, nearly matching human expert performance. We test the performance of StarcNet by applying pre-trained CNN model to galaxies not included in the training set, finding accuracies similar to the reference one. We test the effect of StarcNet predictions on the inferred cluster properties by comparing multi-color luminosity functions and mass-age plots from catalogs produced by StarcNet and by human-labeling; distributions in luminosity, color, and physical characteristics of star clusters are similar for the human and ML classified samples. There are two advantages to the ML approach: (1) reproducibility of the classifications: the ML algorithm's biases are fixed and can be measured for subsequent analysis; and (2) speed of classification: the algorithm requires minutes for tasks that humans require weeks to months to perform. By achieving comparable accuracy to human classifiers, StarcNet will enable extending classifications to a larger number of candidate samples than currently available, thus increasing significantly the statistics for cluster studies.
Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception
Authors: Brian Reily, Hao Zhang
Date: 2020-12-17 00:00:00
Tasks: feature selection, Object Recognition

Collaborative multi-robot perception provides multiple views of an environment, offering varying perspectives to collaboratively understand the environment even when individual robots have poor points of view or when occlusions are caused by obstacles. These multiple observations must be intelligently fused for accurate recognition, and relevant observations need to be selected in order to allow unnecessary robots to continue on to observe other targets. This research problem has not been well studied in the literature yet. In this paper, we propose a novel approach to collaborative multi-robot perception that simultaneously integrates view selection, feature selection, and object recognition into a unified regularized optimization formulation, which uses sparsity-inducing norms to identify the robots with the most representative views and the modalities with the most discriminative features. As our optimization formulation is hard to solve due to the introduced non-smooth norms, we implement a new iterative optimization algorithm, which is guaranteed to converge to the optimal solution. We evaluate our approach through a case-study in simulation and on a physical multi-robot system. Experimental results demonstrate that our approach enables effective collaborative perception through accurate object recognition and effective view and feature selection.
Clique: Spatiotemporal Object Re-identification at the City Scale
Authors: Tiantu Xu, Kaiwen Shen, Yang Fu, Humphrey Shi, Felix Xiaozhu Lin
Date: 2020-12-17 00:00:00
Tasks: Clustering, Image Retrieval, Object, Retrieval

Object re-identification (ReID) is a key application of city-scale cameras. While classic ReID tasks are often considered as image retrieval, we treat them as spatiotemporal queries for locations and times in which the target object appeared. Spatiotemporal reID is challenged by the accuracy limitation in computer vision algorithms and the colossal videos from city cameras. We present Clique, a practical ReID engine that builds upon two new techniques: (1) Clique assesses target occurrences by clustering fuzzy object features extracted by ReID algorithms, with each cluster representing the general impression of a distinct object to be matched against the input; (2) to search in videos, Clique samples cameras to maximize the spatiotemporal coverage and incrementally adds cameras for processing on demand. Through evaluation on 25 hours of videos from 25 cameras, Clique reached a high accuracy of 0.87 (recall at 5) across 70 queries and runs at 830x of video realtime in achieving high accuracy.
Learned Block-based Hybrid Image Compression
Authors: Yaojun Wu, Xin Li, Zhizheng Zhang, Xin Jin, Zhibo Chen
Date: 2020-12-17 00:00:00
Tasks: Blocking, GPU, Image Compression, MS-SSIM, SSIM

Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. First, parallel acceleration of the autoregressive entropy model cannot be achieved due to serial decoding. Second, full-resolution inference often causes the out-of-memory(OOM) problem with limited GPU resources, especially for high-resolution images. Block partition is a good design choice to handle the above issues, but it brings about new challenges in reducing the redundancy between blocks and eliminating block effects. To tackle the above challenges, this paper provides a learned block-based hybrid image compression (LBHIC) framework. Specifically, we introduce explicit intra prediction into a learned image compression framework to utilize the relation among adjacent blocks. Superior to context modeling by linear weighting of neighbor pixels in traditional codecs, we propose a contextual prediction module (CPM) to better capture long-range correlations by utilizing the strip pooling to extract the most relevant information in neighboring latent space, thus achieving effective information prediction. Moreover, to alleviate blocking artifacts, we further propose a boundary-aware postprocessing module (BPM) with the edge importance taken into account. Extensive experiments demonstrate that the proposed LBHIC codec outperforms the VVC, with a bit-rate conservation of 4.1%, and reduces the decoding time by approximately 86.7% compared with that of state-of-the-art learned image compression methods.
Rational function regression method for numerical analytic continuation
Authors: Jian Wang, Sudip Chakravarty
Date: 2018-12-05 00:00:00
Tasks: regression

A simple method for numerical analytic continuation is developed. It is designed to analytically
continue the imaginary time (Matsubara frequency) quantum Monte Carlo simulation results to
the real time (real frequency) domain. Such a method is based on the Pad´e approximation. We
modify it to be a linear regression problem, and then use bootstrapping statistics to get the averaged
result and estimate the error. Unlike maximum entropy method, no prior information is needed.
Test-cases have shown that the spectrum is recovered for inputs with relative error as high as 1%.
Battle royale optimization algorithm
Authors: Taymaz Rahkar-Farshi
Date: 2020-06-02 00:00:00
Tasks: Metaheuristic Optimization

Recently, several metaheuristic optimization approaches have been developed for solving many complex problems in various areas. Most of these optimization algorithms are inspired by nature or the social behavior of some animals. However, there is no optimization algorithm which has been inspired by a game. In this paper, a novel metaheuristic optimization algorithm, named BRO (battle royale optimization), is proposed. The proposed method is inspired by a genre of digital games knowns as “battle royale.” BRO is a population-based algorithm in which each individual is represented by a soldier/player that would like to move toward the safest (best) place and ultimately survive. The proposed scheme has been compared with the well-known PSO algorithm and six recent proposed optimization algorithms on nineteen benchmark optimization functions. Moreover, to evaluate the performance of the proposed algorithm on real-world engineering problems, the inverse kinematics problem of the 6-DOF PUMA 560 robot arm is considered. The experimental results show that, according to both convergence and accuracy, the proposed algorithm is an efficient method and provides promising and competitive results.
Multi-Modal Forest Optimization Algorithm
Authors: Mohanna Orujpour, Mohammad-Reza Feizi-Derakhshi, Taymaz Rahkar-Farshi
Date: 2019-03-05 00:00:00
Tasks: Clustering, Metaheuristic Optimization

Multi-modal optimization algorithms are one of the most challenging issues in the field of optimization. Most real-world problems have more than one solution; therefore, the potential role of multi-modal optimization algorithms is rather significant. Multi-modal problems consider several global and local optima. Therefore, during the search process, most of the points should be detected by the algorithm. The forest optimization algorithm has been recently introduced as a new evolutionary algorithm with the capability of solving unimodal problems. This paper presents the multi-modal forest optimization algorithm (MMFOA), which is constructed by applying a clustering technique, based on niching methods, to the unimodal forest optimization algorithm. The MMFOA operates by dividing the population of the forest into subpopulations to locate existing local and global optima. Subpopulations are generated by the Basic Sequential Algorithmic Scheme with a radius neighborhood. As population size is self-adaptive in MMFOA, population size can be increased in functions with too many local and global optima. The proposed algorithm is evaluated by a set of multi-modal benchmark functions. The experiment results show that not only is the population size low, but also that the convergence speed is high, and that the algorithm is efficient in solving multi-modal problems.
Forest Optimization Algorithm
Authors: Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi
Date: 2014-11-01 00:00:00

In this article, a new evolutionary algorithm, Forest Optimization Algorithm (FOA), suitable for continuous nonlinear optimization problems has been proposed. It is inspired by few trees in the forests which can survive for several decades, while other trees could live for a limited period. In FOA, seeding procedure of the trees is simulated so that, some seeds fall just under the trees, while others are distributed in wide areas by natural procedures and the animals that feed on the seeds or fruits. Application of the proposed algorithm on some benchmark functions demonstrated its good capability in comparison with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Also we tested the performance of FOA on feature weighting as a real optimization problem and the results of the experiments showed the good performance of FOA in some data sets from the UCI repository.
Image Clustering with Optimization Algorithms and Color Space
Authors: Taymaz Rahkar Farshi, Recep Demirci, Mohammad-Reza Feizi-Derakhshi
Date: 2018-04-18 00:00:00
Tasks: Clustering, Image Clustering, Image Segmentation, Semantic Segmentation

In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the number of thresholds. However, the determination of multiple thresholds is a typical issue. Moreover, the conventional thresholding algorithms could not be used in color image segmentation. In this study, a new color image clustering algorithm with multilevel thresholding has been presented and, it has been shown how to use the multilevel thresholding techniques for color image clustering. Thus, initially, threshold selection techniques such as the Otsu and Kapur methods were employed for each color channel separately. The objective functions of both approaches have been integrated with the forest optimization algorithm (FOA) and particle swarm optimization (PSO) algorithm. In the next stage, thresholds determined by optimization algorithms were used to divide color space into small cubes or prisms. Each sub-cube or prism created in the color space was evaluated as a cluster. As the volume of prisms affects the homogeneity of the clusters created, multiple thresholds were employed to reduce the sizes of the sub-cubes. The performance of the proposed method was tested with different images. It was observed that the results obtained were more efficient than conventional methods.
Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems
Authors: Yousef Abdi, Mohammad-Reza Feizi-Derakhshi
Date: 2019-12-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems.
Potential evolutionary advantage of a dissociative search mechanism in DNA mismatch repair
Authors: Kyle Crocker, James London, Andrés Medina, Richard Fishel, Ralf Bundschuh
Date: 2020-12-17 00:00:00

Protein complexes involved in DNA mismatch repair appear to diffuse along dsDNA in order to locate a hemimethylated incision site via a dissociative mechanism. Here, we study the probability that these complexes locate a given target site via a semi-analytic, Monte Carlo calculation that tracks the association and dissociation of the complexes. We compare such probabilities to those obtained using a non-dissociative diffusive scan, and determine that for experimentally observed diffusion constants, search distances, and search durations $\textit{in vitro}$, there is neither a significant advantage nor disadvantage associated with the dissociative mechanism in terms of probability of successful search, and that both search mechanisms are highly efficient for a majority of hemimethylated site distances. Furthermore, we examine the space of physically realistic diffusion constants, hemimethylated site distances, and association lifetimes and determine the regions in which dissociative searching is more or less efficient than non-dissociative searching. We conclude that the dissociative search mechanism is advantageous in the majority of the physically realistic parameter space.
Repairing dynamic models: a method to obtain identifiable and observable reparameterizations with mechanistic insights
Authors: Gemma Massonis, Julio R. Banga, Alejandro F. Villaverde
Date: 2020-12-17 00:00:00

Mechanistic dynamic models allow for a quantitative and systematic interpretation of data and the generation of testable hypotheses. However, these models are often over-parameterized, leading to non-identifiability and non-observability, i.e. the impossibility of inferring their parameters and state variables. The lack of structural identifiability and observability (SIO) compromises a model's ability to make predictions and provide insight. Here we present a methodology, AutoRepar, that corrects SIO deficiencies automatically, yielding reparameterized models that are structurally identifiable and observable. The reparameterization preserves the mechanistic meaning of selected variables, and has the exact same dynamics and input-output mapping as the original model. We implement AutoRepar as an extension of the STRIKE-GOLDD software toolbox for SIO analysis, applying it to several models from the literature to demonstrate its ability to repair their structural deficiencies. AutoRepar increases the applicability of mechanistic models, enabling them to provide reliable information about their parameters and dynamics.
Explicit RKF-Compact Scheme for Pricing Regime Switching American Options with Varying Time Step
Authors: Chinonso Nwankwo, Weizhong Dai
Date: 2020-12-17 00:00:00

In this research work, an explicit Runge-Kutta-Fehlberg (RKF) time integration with a fourth-order compact finite difference scheme in space and a high order analytical approximation of the optimal exercise boundary is employed for solving the regime-switching pricing model. In detail, we recast the free boundary problem into a system of nonlinear partial differential equations with a multi-fixed domain. We then introduce a transformation based on the square root function with a Lipschitz character from which a high order analytical approximation is obtained to compute the derivative of the optimal exercise boundary in each regime. We further compute the boundary values, asset option, and the option Greeks for each regime using fourth-order spatial discretization and adaptive time integration. In particular, the coupled assets options and option Greeks are estimated using Hermite interpolation with Newton basis. Finally, a numerical experiment is carried out with two- and four-regimes examples and results are compared with the existing methods. The results obtained from the numerical experiment show that the present method provides better performance in terms of computational speed and more accurate solutions with a large step size.