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Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement

Overparametrization of HyperNetworks at Fixed FLOP-Count Ena...

引用

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

作者： Muller, Lorenz K. Huawei Technol Zurich Res Ctr Zurich Switzerland

ISBN: (纸本)9781665448994

Deep convolutional neural networks can enhance images taken with small mobile camera sensors and excel at tasks like demoisaicing, denoising and super-resolution. However, for practical use on mobile devices these networks often require too many FLOPs and reducing the FLOPs of a convolution layer, also reduces its parameter count. This is problematic in view of the recent finding that heavily over-parameterized neural networks are often the ones that generalize best. In this paper we propose to use HyperNetworks to break the fixed ratio of FLOPs to parameters of standard convolutions. This allows us to exceed previous state-of-the-art architectures in SSIM and MS-SSIM on the Zurich RAWto-DSLR (ZRR) data-set at > 10x reduced FLOP-count. On ZRR we further observe generalization curves consistent with 'double-descent' behavior at fixed FLOP-count, in the large image limit. Finally we demonstrate the same technique can be applied to an existing network (VDN) to reduce its computational cost while maintaining fidelity on the Smartphone Image Denoising Dataset (SIDD). Code for key functions is given in the supplemental.

关键词： Training Convolution Noise reduction computer architecture Cameras Energy efficiency Sensors

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OmniFlow: Human Omnidirectional Optical Flow

OmniFlow: Human Omnidirectional Optical Flow

引用

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

作者： Seidel, Roman Apitzsch, Andre Hirtz, Gangolf Tech Univ Chemnitz Fac Elect Engn & Informat Technol D-09126 Chemnitz Germany

ISBN: (纸本)9781665448994

Optical flow is the motion of a pixel between at least two consecutive video frames and can be estimated through an end-to-end trainable convolutional neural network. To this end, large training datasets are required to improve the accuracy of optical flow estimation. Our paper presents OmniFlow: a new synthetic omnidirectional human optical flow dataset. Based on a rendering engine we create a naturalistic 3D indoor environment with textured rooms, characters, actions, objects, illumination and motion blur where all components of the environment are shuffled during the data capturing process. The simulation has as output rendered images of household activities and the corresponding forward and backward optical flow. To verify the data for training volumetric correspondence networks for optical flow estimation we train different subsets of the data and test on OmniFlow with and without Test-Time-Augmentation. As a result we have generated 23,653 image pairs and corresponding forward and backward optical flow. Our dataset can be downloaded from: https://***/byfs

关键词： Training Three-dimensional displays Lighting Estimation Network architecture Rendering (computer graphics) Indoor environment

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A General Framework for Jersey Number recognition in Sports Video

A General Framework for Jersey Number Recognition in Sports ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Maria Koshkina James H. Elder York University Toronto Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Jersey number recognition is an important task in sports video analysis, partly due to its importance for long-term player tracking. It can be viewed as a variant of scene text recognition. However, there is a lack of published attempts to apply scene text recognition models on jersey number data. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We address issues of occlusions and assess the degree to which training on one sport (hockey) can be generalized to another (soccer). For the latter, we also consider how jersey number recognition at the single-image level can be aggregated across frames to yield tracklet-level jersey number labels. We demonstrate high performance on image- and tracklet-level tasks, achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code, models, and data are available at https://***/mkoshkina/jerseynumber-pipeline.

关键词： Training computer vision Adaptation models Codes Accuracy Text recognition conferences

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Detecting Stable Keypoints from Events through Image Gradient Prediction

Detecting Stable Keypoints from Events through Image Gradien...

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

作者： Chiberre, Philippe Perot, Etienne Sironi, Amos Lepetit, Vincent PROPHESEE Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9781665448994

We present a method that detects stable keypoints from an event stream at high speed with a low memory footprint. Our key observation connects two points: It should be easier to reconstruct the image gradients rather than the image itself from the events, and the Harris corner detector, one of the most reliable keypoint detectors for short baseline regular images, depends on the image gradients, not the image. We therefore introduce a recurrent convolutional neural network to predict image gradients from events. As image gradients and events are correlated, this prediction task is relatively easy and we can keep this network very small. We train our network solely on synthetic data. Extracting Harris corners from these gradients is then very efficient. Moreover, in contrast to learned methods, we can change the hyperparameters of the detector without retraining. Our experiments confirm that predicting image gradients rather than images is much more efficient, and that our approach predicts stable corner points which are easier to track for a longer time compared to state-of-the-art event-based methods.

关键词： Image analysis Detectors computer architecture Streaming media Cameras pattern recognition Reliability

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Fine-Grained Visual Attribute Extraction from Fashion Wear

Fine-Grained Visual Attribute Extraction from Fashion Wear

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

作者： Parekh, Viral Shaik, Karimulla Biswas, Soma Chelliah, Muthusamy Flipkart Internet Private Ltd Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Automatically extracting visual attributes for e-commerce data has widespread applications in cataloging, catalogue qualification and enrichment, visual search, etc. Here, we address the task of visual attribute extraction for a highly challenging real-world fashion data from Flipkart catalogue (an Indian e-commerce platform), which is collected from seller uploaded product images. This data not only contains widely varying categories (e.g., shirt, sari, shoes), but also has both coarse-grained (e.g., occasion, top type, sari type) and fine-grained (e.g., neck type, print type) attributes. Training examples available for different attributes are highly imbalanced, making this task even more challenging. To this end, we propose an end-to-end framework which integrates multi-task learning with transformer as an attention module, in addition to handling the data imbalance. The proposed architecture supports multiple attributes across various product categories in a scalable manner. Extensive experiments on the in-house dataset shows effectiveness of the proposed framework in improving performance of the fine-grained attributes by 13% on the baseline across the attributes.

关键词： Visualization computer vision conferences Footwear computer architecture Neck Data mining

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vision-based Neural Scene Representations for Spacecraft

Vision-based Neural Scene Representations for Spacecraft

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

作者： Mergy, Anne Lecuyer, Gurvan Derksen, Dawa Izzo, Dario European Space Agcy Noordwijk NL-2201 AZ Noordwijk Netherlands

ISBN: (纸本)9781665448994

In advanced mission concepts with high levels of autonomy, spacecraft need to internally model the pose and shape of nearby orbiting objects. Recent works in neural scene representations show promising results for inferring generic three-dimensional scenes from optical images. Neural Radiance Fields (NeRF) have shown success in rendering highly specular surfaces using a large number of images and their pose. More recently, Generative Radiance Fields (GRAF) achieved full volumetric reconstruction of a scene from unposed images only, thanks to the use of an adversarial framework to train a NeRF. In this paper, we compare and evaluate the potential of NeRF and GRAF to render novel views and extract the 3D shape of two different spacecraft, the Soil Moisture and Ocean Salinity satellite of ESA's Living Planet Programme and a generic cube sat. Considering the best performances of both models, we observe that NeRF has the ability to render more accurate images regarding the material specularity of the spacecraft and its pose. For its part, GRAF generates precise novel views with accurate details even when parts of the satellites are shadowed while having the significant advantage of not needing any information about the relative pose.

关键词： Space vehicles computer vision Surface reconstruction Sea surface Satellites Three-dimensional displays Shape

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DeVLBert: Out-of-distribution Visio-Linguistic Pretraining with Causality

DeVLBert: Out-of-distribution Visio-Linguistic Pretraining w...

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

作者： Zhang, Shengyu Jiang, Tan Wang, Tan Kuang, Kun Zhao, Zhou Zhu, Jianke Yu, Jin Yang, Hongxia Wu, Fei Zhejiang Univ Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose to investigate out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert(1), to perform intervention based-learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of DeVLBert by boosting generalization ability(2).

关键词： Visualization computer vision Correlation conferences Image retrieval computer architecture Knowledge discovery

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AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

AsymmNet: Towards ultralight convolution neural networks usi...

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

作者： Yang, Haojin Shen, Zhen Zhao, Yucheng Hasso Plattner Inst Potsdam Germany Alibaba Cloud Hangzhou Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9781665448994

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. Doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a dropin replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://***/Spark001/AsymmNet

关键词： Convolutional codes computer vision conferences Computational modeling Neural networks computer architecture pattern recognition

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Discovering Multi-Hardware Mobile Models via Architecture Search

Discovering Multi-Hardware Mobile Models via Architecture Se...

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

作者： Chu, Grace Arikan, Okan Bender, Gabriel Wang, Weijun Brighton, Achille Kindermans, Pieter-Jan Liu, Hanxiao Akin, Berkin Gupta, Suyog Howard, Andrew Google LLC Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored. In this paper, we argue that, for applications that may be deployed on multiple hardware, having different single-hardware models across the deployed hardware makes it hard to guarantee consistent outputs across hardware and duplicates engineering work for debugging and fixing. To minimize such deployment cost, we propose an alternative solution, multi-hardware models, where a single architecture is developed for multiple hardware. With thoughtful search space design and incorporating the proposed multi-hardware metrics in neural architecture search, we discover multi-hardware models that give state-of-the-art (SoTA) performance across multiple hardware in both average and worse case scenarios. For performance on individual hardware, the single multi-hardware model yields similar or better results than SoTA performance on accelerators like GPU, DSP and EdgeTPU which was achieved by different models, while having similar performance with MobilenetV3 Large Minimalistic model on mobile CPU.(1)

关键词： computer vision conferences Graphics processing units Focusing computer architecture Debugging Extraterrestrial measurements

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Class-Incremental Experience Replay for Continual Learning under Concept Drift

Class-Incremental Experience Replay for Continual Learning u...

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

作者： Korycki, Lukasz Krawczyk, Bartosz Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

ISBN: (纸本)9781665448994

Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should be stored. Data stream mining focuses on adaptation to concept drift and discarding outdated information, assuming that only the most recent data is relevant. While these two areas are mainly being developed in separation, they offer complementary views on the problem of learning from dynamic data. There is a need for unifying them, by offering architectures capable of both learning and storing new information, as well as revisiting and adapting to changes in previously seen concepts. We propose a novel continual learning approach that can handle both tasks. Our experience replay method is fueled by a centroid-driven memory storing diverse instances of incrementally arriving classes. This is enhanced with a reactive subspace buffer that tracks concept drift occurrences in previously seen classes and adapts clusters accordingly. The proposed architecture is thus capable of both remembering valid and forgetting outdated information, offering a holistic framework for continual learning under concept drift.

关键词： computer vision conferences computer architecture Machine learning Data mining Task analysis

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