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Constellations: A novel dataset for studying iterative inference in humans and AI

Constellations: A novel dataset for studying iterative infer...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Khajuria, Tarun Tulver, Kadi Luik, Taavi Aru, Jaan Univ Tartu Inst Comp Sci Tartu Estonia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Under complex viewing conditions, human perception relies on generating hypotheses and revising them in an iterative fashion. We developed novel visual stimuli to study such iterative inference in humans and AI. In these stimuli, called "constellations", all local information about the object has been removed and it can only be recognized when taking into account the global pattern. We here describe the dataset and demonstrate that humans indeed use an iterative process of generating hypotheses and refining them to solve these images. We also provide code that allows researchers to create their own constellation images. The constellation dataset allows researchers to develop sketching algorithms for guessing the hidden object. As such algorithms used by humans appear to be iterative in nature, this dataset will facilitate the study of iterative inference in minds and machines.

关键词： Visualization Image recognition Machine vision conferences Refining Iterative algorithms Inference algorithms

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Boundary-aware Image Inpainting with Multiple Auxiliary Cues

Boundary-aware Image Inpainting with Multiple Auxiliary Cues

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yamashita, Yohei Shimosato, Kodai Ukita, Norimichi Toyota Technol Inst Nagoya Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image inpainting (a.k.a. image completion) allows us to remove unexpected foreground objects from an observed image and to restore the removed region with background pixels. The performance of image inpainting is improved by auxiliary cues such as edge boundaries and segmentation regions. As a new auxiliary cue, this paper focuses on a depth image that is estimated from an input RGB image by monocular depth estimation. In the depth image, boundaries between different objects (e.g., objects located in different distances) with similar pixel values might be available, while those boundaries are difficult to be detected by edge detection and segmentation. Our proposed method employs those boundaries in the edge and depth images as auxiliary cues. Experiments demonstrate that our proposed method augmented by the depth image outperforms its baseline quantitatively (i.e., 1.17dB and 0.74dB PSNR gains on the Paris-StreetView and Places datasets, respectively) and qualitatively.

关键词： Image segmentation computer vision Image edge detection conferences Estimation pattern recognition Image restoration

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A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement

A Deeper Look into Aleatoric and Epistemic Uncertainty Disen...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Valdenegro-Toro, Matias Mori, Daniel Saromo Univ Groningen Dept AI Bernoulli Inst Groningen Netherlands Pontifical Catholic Univ Peru Artificial Intelligence Res Grp San Miguel Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue. Uncertainty quantification is required for many applications, and disentangled aleatoric and epistemic uncertainties are best. In this paper, we generalize methods to produce disentangled uncertainties to work with different uncertainty quantification methods, and evaluate their capability to produce disentangled uncertainties. Our results show that: there is an interaction between learning aleatoric and epistemic uncertainty, which is unexpected and violates assumptions on aleatoric uncertainty, some methods like Flipout produce zero epistemic uncertainty, aleatoric uncertainty is unreliable in the out-of-distribution setting, and Ensembles provide overall the best disentangling quality. We also explore the error produced by the number of samples hyper-parameter in the sampling softmax function, recommending N > 100 samples. We expect that our formulation and results help practitioners and researchers choose uncertainty methods and expand the use of disentangled uncertainties, as well as motivate additional research into this topic.

关键词： computer vision Uncertainty conferences Neural networks pattern recognition Task analysis

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Curriculum Learning for Data-Efficient vision-Language Alignment

Curriculum Learning for Data-Efficient Vision-Language Align...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Srinivasan, Tejas Ren, Xiang Thomason, Jesse University of Southern California United States

ISBN: (纸本)9798350302493

Aligning image and text encoders from scratch using contrastive learning requires large amounts of paired image-text data. We alleviate this need by aligning individually pre-trained language and vision representation models using a much smaller amount of paired data with a curriculum learning algorithm to learn fine-grained vision-language alignments. TOnICS (Training with Ontology-Informed Contrastive Sampling) initially samples minibatches whose image-text pairs contain a wide variety of objects to learn object-level vision-language alignment, and progressively samples minibatches where all image-text pairs contain the same object to learn finer-grained contextual alignment. Aligning pre-trained BERT and VinVL-OD models to each other using TOnICS outperforms CLIP on downstream zero-shot image retrieval using © 2023 ieee.

关键词： Curricula

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Simulating Task-Free Continual Learning Streams from Existing Datasets

Simulating Task-Free Continual Learning Streams from Existin...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Chrysakis, Aristotelis Moens, Marie-Francine KU Leuven Department of Computer Science Leuven Belgium

ISBN: (纸本)9798350302493

Task-free continual learning is the subfield of machine learning that focuses on learning online from a stream whose distribution changes continuously over time. In contrast, previous works evaluate task-free continual learning using streams with distributions that change not continuously, but only at a few distinct points in time. In order to address the discrepancy between the definition and evaluation of task-free continual learning, we propose a principled algorithm that can permute any labeled dataset into a stream that is continuously nonstationary. We empirically show that the streams generated by our algorithm are less structured than the ones conventionally used in the literature. Moreover, we use our simulated task-free streams to benchmark multiple methods applicable to the task-free setting. We hope that our work will allow other researchers to better evaluate learning performance on continuously nonstationary streams. © 2023 ieee.

关键词： Learning systems

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Visual Semantic Relatedness Dataset for Image Captioning

Visual Semantic Relatedness Dataset for Image Captioning

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Sabir, Ahmed Moreno-Noguer, Francesc Padró, Lluís Universitat Politècnica de Catalunya TALP Research Center Barcelona Spain CSIC-UPC Institut de Ròbotica i Informàtica Industrial Barcelona Spain

ISBN: (纸本)9798350302493

Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story. In this paper, we propose a textual visual context dataset for captioning, in which the publicly available dataset COCO Captions [30] has been extended with information about the scene (such as objects in the image). Since this information has a textual form, it can be used to leverage any NLP task, such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach. Our dataset is available at https://***/ahmedssabir/Textual-Visual-Semantic-Dataset. © 2023 ieee.

关键词： computer vision

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Live Demo: E2P-Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P-Events to Polarization Reconstruction from PD...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Delbruck, Tobi Wang, Zuowen Mei, Haiyang Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Gruev, Viktor Univ. of Zurich and ETH Zurich Sensors Group Institute of Neuroinformatics Switzerland Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria Western Sydney University Intl. Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Dept. of Electrical and Computer Engineering UrbanaIL United States

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [... 详细信息

ISBN: (纸本)9798350302493

关键词： Polarization

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Residual Feature Pyramid Network for Enhancement of Vascular patterns

Residual Feature Pyramid Network for Enhancement of Vascular...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kotwal, Ketan Marcel, Sebastien Idiap Res Inst Martigny Switzerland Univ Lausanne Lausanne Switzerland

ISBN: (纸本)9781665487399

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

关键词： Histograms Protocols Veins Pipelines Fingers computer architecture Network architecture

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Simulated Quantization, Real Power Savings

Simulated Quantization, Real Power Savings

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： van Baalen, Mart Kahne, Brian Mahurin, Eric Kuzmin, Andrey Skliar, Andrii Nagel, Markus Blankevoort, Tijmen Qualcomm AI Res San Diego CA 92121 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reduced precision hardware-based matrix multiplication accelerators are commonly employed to reduce power consumption of neural network inference. Multiplier designs used in such accelerators possess an interesting property: When the same bit is 0 for two consecutive compute cycles, the multiplier consumes less power. In this paper we show that this effect can be used to reduce power consumption of neural networks by simulating low bit-width quantization on higher bit-width hardware. We show that simulating 4 bit quantization on 8 bit hardware can yield up to 17% relative reduction in power consumption on commonly used networks. Furthermore, we show that in this context, bit operations (BOPs) are a good proxy for power efficiency, and that learning mixed-precision configurations that target lower BOPs can achieve better trade-offs between accuracy and power efficiency.

关键词： computer vision Power demand Quantization (signal) conferences Neural networks Hardware pattern recognition

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DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging

DRHDR: A Dual branch Residual Network for Multi-Bracket High...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Marin-Vega, Juan Sloth, Michael Schneider-Kamp, Peter Rottger, Richard Univ Southern Denmark Dept Math & Comp Sci IMADA Odense Denmark Esoft Syst Odense Denmark

ISBN: (纸本)9781665487399

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging. To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions. The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details. A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts. By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.

关键词： computer vision conferences Imaging Dynamic range pattern recognition Complexity theory Convolutional neural networks

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