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Improving Normalization with the James-Stein Estimator

Improving Normalization with the James-Stein Estimator

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Seyedalireza Khoshsirat Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab University of Delaware

Stein’s paradox holds considerable sway in high-dimensional statistics, highlighting that the sample mean, traditionally considered the de facto estimator, might not be the most efficacious in higher dimensions. To address this, the James-Stein estimator proposes an enhancement by steering the sample means toward a more centralized mean vector. In this paper, first, we establish that normalization layers in deep learning use inadmissible estimators for mean and variance. Next, we introduce a novel method to employ the James-Stein estimator to improve the estimation of mean and variance within normalization layers. We evaluate our method on different computer vision tasks: image classification, semantic segmentation, and 3D object classification. Through these evaluations, it is evident that our improved normalization layers consistently yield superior accuracy across all tasks without extra computational burden. Moreover, recognizing that a plethora of shrinkage estimators surpass the traditional estimator in performance, we study two other prominent shrinkage estimators: Ridge and LASSO. Additionally, we provide visual representations to intuitively demonstrate the impact of shrinkage on the estimated layer statistics. Finally, we study the effect of regularization and batch size on our modified batch normalization. The studies show that our method is less sensitive to batch size and regularization, improving accuracy under various setups.

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Embedding Attention Blocks For Answer Grounding

Embedding Attention Blocks For Answer Grounding

引用

IEEE International Conference on image Processing

作者： Seyedalireza Khoshsirat Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab University of Delaware

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Despite the introduction of various attention methods for the answer grounding task, they often encounter three common challenges. Firstly, some designs lack the capability to utilize pre-trained networks and fail to benefit from extensive data pre-training. Secondly, certain custom designs are not based on well-established previous models, limiting the network’s learning potential. Lastly, complex designs that impede reimplementation or enhancement. To address these issues, this paper presents a novel architectural block, termed the Embedding Attention Block (EAB). This block re-calibrates channel-wise image feature-maps by explicitly modeling inter-dependencies between the image feature-maps and the image-question-answer embeddings. The visual demonstration showcases how this block filters out irrelevant feature-map channels based on embeddings. Our approach builds upon three key concepts: Semantic Region Proposal, Input Embedding, and Dynamic Region Fusion. We validate our method’s efficacy using the TextVQA-X, VQS, VQA-X, and VizWiz-VQA-Grounding datasets, and carry out several ablation studies to show the effectiveness of our design choices. Notably, our novel network ranked first place on the 2023 VizWiz-VQA-Grounding challenge leaderboard and won the challenge.

关键词： Visualization Limiting Filters Grounding image processing Semantics Proposals

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Sentence Attention Blocks for Answer Grounding

Sentence Attention Blocks for Answer Grounding

引用

International Conference on Computer Vision (ICCV)

作者： Seyedalireza Khoshsirat Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab University of Delaware

Answer grounding is the task of locating relevant visual evidence for the Visual Question Answering task. While a wide variety of attention methods have been introduced for this task, they suffer from the following three problems: designs that do not allow the usage of pre-trained networks and do not benefit from large data pre-training, custom designs that are not based on well-grounded previous designs, therefore limiting the learning power of the network, or complicated designs that make it challenging to re-implement or improve them. In this paper, we propose a novel architectural block, which we term Sentence Attention Block, to solve these problems. The proposed block re-calibrates channel-wise image feature-maps by explicitly modeling inter-dependencies between the image feature-maps and sentence embedding. We visually demonstrate how this block filters out irrelevant feature-maps channels based on sentence embedding. We start our design with a well-known attention method, and by making minor modifications, we improve the results to achieve state-of-the-art accuracy. The flexibility of our method makes it easy to use different pre-trained backbone networks, and its simplicity makes it easy to understand and be re-implemented. We demonstrate the effectiveness of our method on the TextVQA-X, VQS, VQA-X, and VizWiz-VQA-Grounding datasets. We perform multiple ablation studies to show the effectiveness of our design choices.

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A Benchmark Grocery Dataset of Realworld Point Clouds From Single View

A Benchmark Grocery Dataset of Realworld Point Clouds From S...

引用

International Conference on 3D Imaging, modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Shivanand Venkanna Sheshappanavar Tejas Anvekar Shivanand Kundargi Yufan Wang Chandra Kambhamettu Dept. of Computer and Information Sciences Video/Image Modeling and Synthesis (VIMS) Lab University of Delaware Newark DE USA

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Fine-grained grocery object recognition is an important computer vision problem with broad applications in automatic checkout, in-store robotic navigation, and assistive technologies for the visually impaired. Existing datasets on groceries are mainly 2D images. Models trained on these datasets are limited to learning features from the regular 2D grids. While portable 3D sensors such as Kinect were commonly available for mobile phones, sensors such as LiDAR and TrueDepth, have recently been integrated into mobile phones. Despite the availability of mobile 3D sensors, there are currently no dedicated real-world large-scale benchmark 3D datasets for grocery. In addition, existing 3D datasets lack fine-grained grocery categories and have limited training samples. Furthermore, collecting data by going around the object versus the traditional photo capture makes data collection cumbersome. Thus, we introduce a large-scale grocery dataset called 3DGrocery100. It constitutes 100 classes, with a total of 87,898 3D point clouds created from 10,755 RGB-D single-view images. We benchmark our dataset on six recent stateof-the-art 3D point cloud classification models. Additionally, we also benchmark the dataset on few-shot and continual learning point cloud classification tasks. Project Page: https://***/3DGrocery100/.

关键词： Point cloud compression Training Solid modeling Three-dimensional displays Benchmark testing Robot sensing systems Mobile handsets

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3DSAINT Representation for 3D Point Clouds

3DSAINT Representation for 3D Point Clouds

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab. Computer and Information Sciences University of Delaware Newark Delaware

This paper introduces a Sphere-based representation to model a 3D scene and show its performance on various tasks, including Structure from Motion (SfM) and 3D scene classification. A significant target application of this work is Mixed Reality, where 3D data can be efficiently represented, and synthetic and real data can be mixed for an immersive experience. Over the past few decades, 3D big data has garnered increased attention in computer vision. Acquiring, representing, reconstructing, querying, classifying, and visualizing 3D models for Mixed Reality has become crucial for many applications, such as medicine, architecture, entertainment, and bioinformatics. With the ever-increasing amount of data that the 3D scanners produce, storing, processing, and transmitting the data becomes challenging. Techniques that exploit the shape information need to be developed to model, classify and visualize the data. Our work offers a novel multi-scale surface representation based on spheres, with the ultimate goal of helping scientists to see and work with 3D data in Mixed Reality more effectively and efficiently.

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A Novel Local Geometry Capture in Pointnet++ for 3D Classification

A Novel Local Geometry Capture in Pointnet++ for 3D Classifi...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Shivanand Venkanna Sheshappanavar Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab. University of Delaware Newark DE USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Few of the recent deep learning models for 3D point sets classification are dependent on how well the model captures the local geometric structures. PointNet++ model made remarkable progress in learning local geometric structures than its predecessor PointNet. It recursively applies Point-Net on nested partitions of the input 3D point set. Point-Net++ model was able to extract the local region features from points by ball querying the local neighborhoods. However, ball querying is less effective in capturing local neigh-borhoods of high curvature surfaces or regions. In this paper, we demonstrate improvement in the 3D classification results by using ellipsoid querying around centroids, capturing more points in the local neighborhood. We extend the ellipsoid querying technique by orienting it in the direction of principal axes of the local neighborhood for better capture of the local geometry. We then take the union of points grouped by ball querying and ellipsoid querying with re-orientation to improve the PointNet++ classification results by 1.1%. Furthermore, we demonstrate the impact of re-oriented ellipsoid querying on a state-of-the-art ball query-based model, Relation-Shape Convolutional Neural Network (RS-CNN), with a 0.8% improvement in classification accuracy on ModelNet40 dataset.

关键词： Three-dimensional displays Ellipsoids Solid modeling Feature extraction Shape Computational modeling

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cPSITRES: A collaborative system for analysis of Big Data on sea ice

cPSITRES: A collaborative system for analysis of Big Data on...

引用

IEEE International Conference on Big Data

作者： Vinit Veerendraveer Singh Scott Sorensen Chandra Kambhamettu Video/Image Modeling and Synthesis Lab University of Delaware Newark Delaware U.S.A Vision Systems Inc. Rhode Island U.S.A

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Analysis of sea ice image rasters presents exciting challenges to the computer vision community, and results from such visual analysis can offer novel insights to domain experts studying sea ice. In recent years, there is a surge in the production of geospatial data sets on sea ice along with the methods for their analysis. As numerous providers independently provision geospatial data sets, they are found in varying formats. Also, there is often limited collaboration between the intra-domain and inter-domain experts analyzing these data sets. To tackle these aforementioned challenges, we have developed the cPSITRES system using open-source technologies and libraries to serve cruise-based high-resolution spatiotemporal Big Data on sea ice along with relevant unstructured public data. The system enables its users to perform complex analysis on the provided data sets and also serves as a collaborative platform for the rapid discovery of open-source methods analyzing sea ice. We also showcase a few exemplary traditional and state-of-the-art computer vision methods for the analysis of data provided by cPSITRES.

关键词： Sea ice Big Data Arctic Computer vision Visualization Analytical models

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IMPROVING CALIBRATION OF THERMAL STEREO CAMERAS USING HEATED CALIBRATION BOARD

IMPROVING CALIBRATION OF THERMAL STEREO CAMERAS USING HEATED...

引用

IEEE International Conference on image Processing

作者： Philip Saponaro Scott Sorensen Stephen Rhein Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab Department of Computer Science University of Delaware

ISBN: (纸本)9781479983407

Calibration of stereo cameras is important for accurate 3D reconstruction. For standard color cameras there are many available tools and algorithms for accurate calibration, such as detecting corners of chessboard patterns on planar calibration boards. When viewed in thermal imagery, these chessboard patterns are difficult to detect due to uniform temperature between the white and black squares. Previous techniques involve creating a custom calibration board using multiple materials. We propose improvements to a method that does not require a custom calibration board. Our method is made more reliable by using an iterative pre-processing technique to enhance contrast and a ceramic tile backing to retain heat longer. We present results which show our calibration board retains heat to reliably detect corners for over 10 minutes;our method performs well in real calibration trials.

关键词： three-dimensional displays Calibration color camera Stereo cameras corners uniform temperatures Cameras ceramic tiles Solar Terrestrial Relations Observatory Board heat Thermal

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REFRACTIVE STEREO RAY TRACING FOR RECONSTRUCTING UNDERWATER STRUCTURES

REFRACTIVE STEREO RAY TRACING FOR RECONSTRUCTING UNDERWATER ...

引用

IEEE International Conference on image Processing

作者： Scott Sorensen Abhishek Kolagunda Philip Saponaro Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab Computer and Information Sciences University of Delaware

ISBN: (纸本)9781479983407

Underwater objects behind a refractive surface pose problems for traditional 3D reconstruction techniques. Scenes where underwater objects are visible from the surface are commonplace, however the refraction of light causes 3D points in these scenes to project non-linearly. Refractive Stereo Ray Tracing allows for accurate reconstruction by modeling the refraction of light. Our approach uses techniques from ray tracing to compute the 3D position of points behind a refractive surface. This technique aims to reconstruct underwater structures in situations where access to the water is dangerous or cost prohibitive. Experimental results in real and synthetic scenes show this technique effectively handles refraction.

关键词： Underwater Stereo Vision Refraction Underwater refractions Stereoviews REFRACTION Light refraction Ray tracing Underwater structures Solar Terrestrial Relations Observatory

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Material Classification with Thermal imagery

Material Classification with Thermal Imagery

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Philip Saponaro Scott Sorensen Abhishek Kolagunda Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab Dept. of Computer and Information Science University of Delaware

ISBN: (纸本)9781467369657

Material classification is an important area of research in computer vision. Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversity of colors in a single material class. In this work we study the use of long wave infrared (i.e. thermal) imagery for material classification. Thermal imagery has the benefit of relative invariance to color changes, invariance to lighting conditions, and can even work in the dark. We collect a database of 21 different material classes with both color and thermal imagery. We develop a set of features that describe water permeation and heating/cooling properties, and test several variations on these methods to obtain our final classifier. The results show that the proposed method outperforms typical color and texture features, and when combined with color information, the results are improved further.

关键词： Thermography Color Classes of materials invariance chromatic information Calculus of variation images computer vision

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