pwc-archive/papers-with-abstracts huggingface dataset:
Learning Distinctive Margin toward Active Domain Adaptation
Authors: Ming Xie, Yuxi Li, Yabiao Wang, Zekun Luo, Zhenye Gan, Zhongyi Sun, Mingmin Chi, Chengjie Wang, Pei Wang
Date: 2022-03-11 00:00:00
Proceeding: CVPR 2022 1
Tasks: Active Learning, Domain Adaptation

Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data. Nevertheless, most active learning methods are not inherently designed to handle domain gap between data distribution, on the other hand, some active domain adaptation methods (ADA) usually requires complicated query functions, which is vulnerable to overfitting. In this work, we propose a concise but effective ADA method called Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and a margin sampling algorithm for data selection. We provide theoretical analysis to show that SDM works like a Support Vector Machine, storing hard examples around decision boundaries and exploiting them to find informative and transferable data. In addition, we propose two variants of our method, one is designed to adaptively adjust the gradient from margin loss, the other boosts the selectivity of margin sampling by taking the gradient direction into account. We benchmark SDM with standard active learning setting, demonstrating our algorithm achieves competitive results with good data scalability. Code is available at https://github.com/TencentYoutuResearch/ActiveLearning-SDM
Information-Theoretic Odometry Learning
Authors: Sen Zhang, Jing Zhang, DaCheng Tao
Date: 2022-03-11 00:00:00

In this paper, we propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation, a crucial component of many robotics and vision tasks such as navigation and virtual reality where relative camera poses are required in real time. We formulate this problem as optimizing a variational information bottleneck objective function, which eliminates pose-irrelevant information from the latent representation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language. Specifically, we bound the generalization errors of the deep information bottleneck framework and the predictability of the latent representation. These provide not only a performance guarantee but also practical guidance for model design, sample collection, and sensor selection. Furthermore, the stochastic latent representation provides a natural uncertainty measure without the needs for extra structures or computations. Experiments on two well-known odometry datasets demonstrate the effectiveness of our method.
Evaluating U-net Brain Extraction for Multi-site and Longitudinal Preclinical Stroke Imaging
Authors: Erendiz Tarakci, Joseph Mandeville, Fahmeed Hyder, Basavaraju G. Sanganahalli, Daniel R. Thedens, Ali Arbab, Shuning Huang, Adnan Bibic, Jelena Mihailovic, Andreia Morais, Jessica Lamb, Karisma Nagarkatti, Marcio A. Dinitz, Andre Rogatko, Arthur W. Toga, Patrick Lyden, Cenk Ayata, Ryan P. Cabeen
Date: 2022-03-11 00:00:00

Rodent stroke models are important for evaluating treatments and understanding the pathophysiology and behavioral changes of brain ischemia, and magnetic resonance imaging (MRI) is a valuable tool for measuring outcome in preclinical studies. Brain extraction is an essential first step in most neuroimaging pipelines; however, it can be challenging in the presence of severe pathology and when dataset quality is highly variable. Convolutional neural networks (CNNs) can improve accuracy and reduce operator time, facilitating high throughput preclinical studies. As part of an ongoing preclinical stroke imaging study, we developed a deep-learning mouse brain extraction tool by using a U-net CNN. While previous studies have evaluated U-net architectures, we sought to evaluate their practical performance across data types. We ask how performance is affected with data across: six imaging centers, two time points after experimental stroke, and across four MRI contrasts. We trained, validated, and tested a typical U-net model on 240 multimodal MRI datasets including quantitative multi-echo T2 and apparent diffusivity coefficient (ADC) maps, and performed qualitative evaluation with a large preclinical stroke database (N=1,368). We describe the design and development of this system, and report our findings linking data characteristics to segmentation performance. We consistently found high accuracy and ability of the U-net architecture to generalize performance in a range of 95-97% accuracy, with only modest reductions in performance based on lower fidelity imaging hardware and brain pathology. This work can help inform the design of future preclinical rodent imaging studies and improve their scalability and reliability.
Social Networks and Spatial Mobility: Evidence from Facebook in India
Authors: Harshil Sahai, Mike Bailey
Date: 2022-03-10 00:00:00
Tasks: Descriptive

This paper studies the role of social networks in spatial mobility across India. Using aggregated and de-identified data from the world's largest online social network, we (i) document new descriptive findings on the structure of social networks and spatial mobility in India; (ii) quantify the effects of social networks on annual migration choice; and (iii) embed these estimates in a spatial equilibrium model to study the wage implications of increasing social connectedness. Across millions of individuals, we find that multiple measures of social capital are concentrated among the rich and educated and among migrants. Across destinations, both mobility patterns and social networks are concentrated toward richer areas. A model of migration suggests individuals are indifferent between a 10% increase in destination wages and a 12-16% increase in destination social networks. Accounting for networks reduces the migration-distance relationship by 19%. In equilibrium, equalizing social networks across locations improves average wages by 3% (24% for the bottom wage-quartile), a larger impact than removing the marginal cost of distance. We find evidence of an economic support mechanism, with destination economic improvements reducing the migration-network elasticity. We also find suggestive evidence for an emotional support mechanism from qualitative surveys among Facebook users. Difference-in-difference estimates suggest college attendance delivers a 20% increase in network size and diversity. Taken together, our data suggest that - by reducing effective moving costs - increasing social connectedness across space may have considerable economic gains.
Labor Demand on a Tight Leash
Authors: Mario Bossler, Martin Popp
Date: 2022-03-10 00:00:00

We develop a labor demand model that encompasses pre-match hiring cost arising from tight labor markets. Through the lens of the model, we study the effect of labor market tightness on firms' labor demand by applying novel shift-share instruments to the universe of German firms. In line with theory, we find that a doubling in tightness reduces firms' employment by 5 percent. Taking into account the resulting search externalities, the wage elasticity of firms' labor demand reduces from -0.7 to -0.5 through reallocation effects. In light of our results, pre-match hiring cost amount to 40 percent of annual wage payments.
Computational Image-based Stroke Assessment for Evaluation of Cerebroprotectants with Longitudinal and Multi-site Preclinical MRI
Authors: Ryan P. Cabeen, Joseph Mandeville, Fahmeed Hyder, Basavaraju G. Sanganahalli, Daniel R. Thedens, Ali Arbab, Shuning Huang, Adnan Bibic, Erendiz Tarakci, Jelena Mihailovic, Andreia Morais, Jessica Lamb, Karisma Nagarkatti, Arthur W. Toga, Patrick Lyden, Cenk Ayata
Date: 2022-03-11 00:00:00

While ischemic stroke is a leading cause of death worldwide, there has been little success translating putative cerebroprotectants from rodent preclinical trials to human patients. We investigated computational image-based assessment tools for practical improvement of the quality, scalability, and outlook for large scale preclinical screening for potential therapeutic interventions in rodent models. We developed, evaluated, and deployed a pipeline for image-based stroke outcome quantification for the Stroke Preclinical Assessment Network (SPAN), a multi-site, multi-arm, multi-stage study evaluating a suite of cerebroprotectant interventions. Our fully automated pipeline combines state-of-the-art algorithmic and data analytic approaches to assess stroke outcomes from multi-parameter MRI data collected longitudinally from a rodent model of middle cerebral artery occlusion (MCAO), including measures of infarct volume, brain atrophy, midline shift, and data quality. We applied our approach to 1,368 scans and report population level results of lesion extent and longitudinal changes from injury. We validated our system by comparison with both manual annotations of coronal MRI slices and tissue sections from the same brain, using crowdsourcing from blinded stroke experts from the network. Our results demonstrate the efficacy and robustness of our image-based stroke assessments. The pipeline may provide a promising resource for ongoing rodent preclinical studies conducted by SPAN and other networks in the future.
Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World
Authors: Sen Zhang, Jing Zhang, DaCheng Tao
Date: 2022-03-11 00:00:00
Tasks: Monocular Visual Odometry, Visual Odometry

Monocular visual odometry (VO) has attracted extensive research attention by providing real-time vehicle motion from cost-effective camera images. However, state-of-the-art optimization-based monocular VO methods suffer from the scale inconsistency problem for long-term predictions. Deep learning has recently been introduced to address this issue by leveraging stereo sequences or ground-truth motions in the training dataset. However, it comes at an additional cost for data collection, and such training data may not be available in all datasets. In this work, we propose VRVO, a novel framework for retrieving the absolute scale from virtual data that can be easily obtained from modern simulation environments, whereas in the real domain no stereo or ground-truth data are required in either the training or inference phases. Specifically, we first train a scale-aware disparity network using both monocular real images and stereo virtual data. The virtual-to-real domain gap is bridged by using an adversarial training strategy to map images from both domains into a shared feature space. The resulting scale-consistent disparities are then integrated with a direct VO system by constructing a virtual stereo objective that ensures the scale consistency over long trajectories. Additionally, to address the suboptimality issue caused by the separate optimization backend and the learning process, we further propose a mutual reinforcement pipeline that allows bidirectional information flow between learning and optimization, which boosts the robustness and accuracy of each other. We demonstrate the effectiveness of our framework on the KITTI and vKITTI2 datasets.
Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding
Authors: Yidan Sun, Qin Chao, Yangfeng Ji, Boyang Li
Date: 2022-03-11 00:00:00
Tasks: Retrieval, Text Retrieval, Video-Text Retrieval

Despite recent advances of AI, story understanding remains an open and under-investigated problem. We collect, preprocess, and publicly release a video-language story dataset, Synopses of Movie Narratives (SyMoN), containing 5,193 video summaries of popular movies and TV series with a total length of 869 hours. SyMoN captures naturalistic storytelling videos made by human creators and intended for a human audience. As a prototypical and naturalistic story dataset, SyMoN features high coverage of multimodal story events and abundant mental-state descriptions. Its use of storytelling techniques cause cross-domain semantic gaps that provide appropriate challenges to existing models. We establish benchmarks on video-text retrieval and zero-shot alignment on movie summary videos, which showcase the importance of in-domain data and long-term memory in story understanding. With SyMoN, we hope to lay the groundwork for progress in multimodal story understanding.
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond
Authors: Tiange Xiang, Chaoyi Zhang, Xinyi Wang, Yang song, Dongnan Liu, Heng Huang, Weidong Cai
Date: 2022-03-11 00:00:00
Tasks: Decoder, Medical Image Analysis, Medical Image Segmentation, Neural Architecture Search, Segmentation

U-Net, as an encoder-decoder architecture with forward skip connections, has achieved promising results in various medical image analysis tasks. Many recent approaches have also extended U-Net with more complex building blocks, which typically increase the number of network parameters considerably. Such complexity makes the inference stage highly inefficient for clinical applications. Towards an effective yet economic segmentation network design, in this work, we propose backward skip connections that bring decoded features back to the encoder. Our design can be jointly adopted with forward skip connections in any encoder-decoder architecture forming a recurrence structure without introducing extra parameters. With the backward skip connections, we propose a U-Net based network family, namely Bi-directional O-shape networks, which set new benchmarks on multiple public medical imaging segmentation datasets. On the other hand, with the most plain architecture (BiO-Net), network computations inevitably increase along with the pre-set recurrence time. We have thus studied the deficiency bottleneck of such recurrent design and propose a novel two-phase Neural Architecture Search (NAS) algorithm, namely BiX-NAS, to search for the best multi-scale bi-directional skip connections. The ineffective skip connections are then discarded to reduce computational costs and speed up network inference. The finally searched BiX-Net yields the least network complexity and outperforms other state-of-the-art counterparts by large margins. We evaluate our methods on both 2D and 3D segmentation tasks in a total of six datasets. Extensive ablation studies have also been conducted to provide a comprehensive analysis for our proposed methods.
Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining
Authors: Kai Zhao, Lei Shen, Yingyi Zhang, Chuhan Zhou, Tao Wang, Ruixin Zhang, Shouhong Ding, Wei Jia, Wei Shen
Date: 2022-03-11 00:00:00
Tasks: TAR

Palmprints are private and stable information for biometric recognition. In the deep learning era, the development of palmprint recognition is limited by the lack of sufficient training data. In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases. Concretely, we introduce an intuitive geometric model which represents palmar creases with parameterized B\'ezier curves. By randomly sampling B\'ezier parameters, we can synthesize massive training samples of diverse identities, which enables us to pretrain large-scale palmprint recognition models. Experimental results demonstrate that such synthetically pretrained models have a very strong generalization ability: they can be efficiently transferred to real datasets, leading to significant performance improvements on palmprint recognition. For example, under the open-set protocol, our method improves the strong ArcFace baseline by more than 10\% in terms of TAR@1e-6. And under the closed-set protocol, our method reduces the equal error rate (EER) by an order of magnitude.
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark
Authors: Stephen Tyree, Jonathan Tremblay, Thang To, Jia Cheng, Terry Mosier, Jeffrey Smith, Stan Birchfield
Date: 2022-03-11 00:00:00
Tasks: Pose Estimation, Robotic Grasping

We present a new dataset for 6-DoF pose estimation of known objects, with a focus on robotic manipulation research. We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation. We provide 3D scanned textured models of these objects, suitable for generating synthetic training data, as well as RGBD images of the objects in challenging, cluttered scenes exhibiting partial occlusion, extreme lighting variations, multiple instances per image, and a large variety of poses. Using semi-automated RGBD-to-model texture correspondences, the images are annotated with ground truth poses accurate within a few millimeters. We also propose a new pose evaluation metric called ADD-H based on the Hungarian assignment algorithm that is robust to symmetries in object geometry without requiring their explicit enumeration. We share pre-trained pose estimators for all the toy grocery objects, along with their baseline performance on both validation and test sets. We offer this dataset to the community to help connect the efforts of computer vision researchers with the needs of roboticists.
Toward Efficient Hyperspectral Image Processing inside Camera Pixels
Authors: Gourav Datta, Zihan Yin, Ajey Jacob, Akhilesh R. Jaiswal, Peter A. Beerel
Date: 2022-03-11 00:00:00

Hyperspectral cameras generate a large amount of data due to the presence of hundreds of spectral bands as opposed to only three channels (red, green, and blue) in traditional cameras. This requires a significant amount of data transmission between the hyperspectral image sensor and a processor used to classify/detect/track the images, frame by frame, expending high energy and causing bandwidth and security bottlenecks. To mitigate this problem, we propose a form of processing-in-pixel (PIP) that leverages advanced CMOS technologies to enable the pixel array to perform a wide range of complex operations required by the modern convolutional neural networks (CNN) for hyperspectral image recognition (HSI). Consequently, our PIP-optimized custom CNN layers effectively compress the input data, significantly reducing the bandwidth required to transmit the data downstream to the HSI processing unit. This reduces the average energy consumption associated with pixel array of cameras and the CNN processing unit by 25.06x and 3.90x respectively, compared to existing hardware implementations. Our custom models yield average test accuracies within 0.56% of the baseline models for the standard HSI benchmarks.
Innovation Diffusion among Case-based Decision-makers
Authors: Benson Tsz Kin Leung
Date: 2022-03-11 00:00:00

This paper analyzes a model of innovation diffusion with case-based individuals a la Gilboa and Schmeidler (1995,1996,1997), who decide whether to consume an incumbent or a new product based on their and their social neighbors' previous consumption experiences. I analyze how diffusion pattern changes with individual characteristics, innovation characteristics and social network. In particular, radical innovation leads to higher initial speed but lower acceleration compared to increment innovation. Social network with stronger overall social tie, lower degree of homophily or higher exposure of reviews from early adopters speed up diffusion of innovation.
Scoring Strategic Agents
Authors: Ian Ball
Date: 2019-09-04 00:00:00
Tasks: scoring rule

I introduce a model of predictive scoring. A receiver wants to predict a sender's quality. An intermediary observes multiple features of the sender and aggregates them into a score. Based on the score, the receiver makes a decision. The sender prefers "higher" decisions, and she can distort each feature at a privately known cost. I characterize the scoring rule that maximizes decision accuracy. This rule underweights some features to deter sender distortion, and overweights other features so that the score is correct on average. The receiver prefers this scoring rule to full disclosure because it mitigates his commitment problem.
PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation
Authors: Lihao Liu, Zhening Huang, Pietro Liò, Carola-Bibiane Schönlieb, Angelica I. Aviles-Rivero
Date: 2022-03-10 00:00:00
Tasks: Image Registration, Medical Image Registration

Medical image registration and segmentation are critical tasks for several clinical procedures. Manual realisation of those tasks is time-consuming and the quality is highly dependent on the level of expertise of the physician. To mitigate that laborious task, automatic tools have been developed where the majority of solutions are supervised techniques. However, in medical domain, the strong assumption of having a well-representative ground truth is far from being realistic. To overcome this challenge, unsupervised techniques have been investigated. However, they are still limited in performance and they fail to produce plausible results. In this work, we propose a novel unified unsupervised framework for image registration and segmentation that we called PC-SwinMorph. The core of our framework is two patch-based strategies, where we demonstrate that patch representation is key for performance gain. We first introduce a patch-based contrastive strategy that enforces locality conditions and richer feature representation. Secondly, we utilise a 3D window/shifted-window multi-head self-attention module as a patch stitching strategy to eliminate artifacts from the patch splitting. We demonstrate, through a set of numerical and visual results, that our technique outperforms current state-of-the-art unsupervised techniques.
Deep Multimodal Guidance for Medical Image Classification
Authors: Mayur Mallya, Ghassan Hamarneh
Date: 2022-03-10 00:00:00
Tasks: Brain Tumor Classification, Classification, Diagnostic, image-classification, Image Classification, Lesion Classification, Medical Image Classification, Skin Lesion Classification

Medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-feasible yet under-performing (inferior) modality and steer it towards improved performance. We focus on the application of deep learning for image-based diagnosis. We develop a light-weight guidance model that leverages the latent representation learned from the superior modality, when training a model that consumes only the inferior modality. We examine the advantages of our method in the context of two clinical applications: multi-task skin lesion classification from clinical and dermoscopic images and brain tumor classification from multi-sequence magnetic resonance imaging (MRI) and histopathology images. For both these scenarios we show a boost in diagnostic performance of the inferior modality without requiring the superior modality. Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference.