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Data-Efficient and Robust Task Selection for Meta-Learning

Data-Efficient and Robust Task Selection for Meta-Learning

引用

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

作者： Donglin Zhan James Anderson Columbia University New York NY

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Meta-learning methods typically learn tasks under the assumption that all tasks are equally important. However, this assumption is often not valid. In real-world applications, tasks can vary both in their importance during different training stages and in whether they contain noisy labeled data or not, making a uniform approach suboptimal. To address these issues, we propose the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be incorporated into both gradient and metric-based meta-learning algorithms. DERTS selects weighted subsets of tasks from task pools by minimizing the approximation error of the full gradient of task pools in the meta-training stage. The selected tasks are efficient for rapid training and robust towards noisy label scenarios. Unlike existing algorithms, DERTS does not require any architecture modification for training and can handle noisy label data in both the support and query sets. Analysis of DERTS shows that the algorithm follows similar training dynamics as learning on the full task pools. Experiments show that DERTS outperforms existing sampling strategies for meta-learning on both gradient-based and metric-based meta-learning algorithms in limited data budget and noisy task settings.

关键词： Metalearning Training computer vision Heuristic algorithms conferences computer architecture Approximation algorithms

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QuantNAS: Quantization-aware Neural Architecture Search For Efficient Deployment On Mobile Device

QuantNAS: Quantization-aware Neural Architecture Search For ...

引用

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

作者： Tianxiao Gao Li Guo Shanwei Zhao Peihan Xu Yukun Yang Xionghao Liu Shihao Wang Shiai Zhu Dajiang Zhou Ant Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep convolutional networks are increasingly applied in mobile AI scenarios. To achieve efficient deployment, researchers combine neural architecture search (NAS) and quantization to find the best quantized architecture. However, existing methods overlook the on-device implementation of quantization. The searching result is usually sub-optimal or has limited latency reduction. To this end, we propose QuantNAS, a novel quantization-aware NAS based on a two-stage one-shot method. Different from the previous method, our method considers the on-device implementation of the quantized network and searches for the architecture from a fully quantized supernet. During training, we propose a batch-statistics-based strategy to alleviate the non-convergence problem. Besides, a scale predictor is proposed and is jointly trained with the supernet. During search, the scale predictor can provide optimal scale for different subnets without retraining. At different latency levels on Kirin 9000 mobile CPU, the proposed method achieves 1.53%-1.68% Top-1 accuracy improvement on ImageNet 1K dataset and 1.7% mAP improvement.

关键词： Training computer vision Quantization (signal) conferences Computational modeling computer architecture Mobile handsets

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Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Deep Convolutional Sparse Coding Networks for Interpretable ...

引用

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

作者： Zixiang Zhao Jiangshe Zhang Haowen Bai Yicheng Wang Yukun Cui Lilun Deng Kai Sun Chunxia Zhang Junmin Liu Shuang Xu Xi’an Jiaotong University Computer Vision Lab ETH Zurich The University of Melbourne Research and Development Institute of Northwestern Polytechnical University in Shenzhen Northwestern Polytechnical University

Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-spectral image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of CSCF use with regard to quantitative evaluation and visual inspection.

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VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer

VT-Former: An Exploratory Study on Vehicle Trajectory Predic...

引用

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

作者： Armin Danesh Pazho Ghazal Alinezhad Noghre Vinit Katariya Hamed Tabkhi University of North Carolina Charlotte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Enhancing roadway safety has become an essential computer vision focus area for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle Trajectory Prediction (VTP) aims to forecast a vehicle’s future positions based on its past and current movements. VTP is a pivotal element for road safety, aiding in applications such as traffic management, accident prevention, work-zone safety, and energy optimization. While most works in this field focus on autonomous driving, with the growing number of surveillance cameras, another sub-field emerges for surveillance VTP with its own set of challenges. In this paper, we introduce VT-Former, a novel transformer-based VTP approach for highway safety and surveillance. In addition to utilizing transformers to capture long-range temporal patterns, a new Graph Attentive Tokenization (GAT) module has been proposed to capture intricate social interactions among vehicles. This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP. Our investigation, conducted across three benchmark datasets from diverse surveillance viewpoints, showcases the State-of-the-Art (SotA) or comparable performance of VT-Former in predicting vehicle trajectories. This study underscores the potential of VT-Former and its architecture, opening new avenues for future research and exploration.

关键词： Road transportation computer vision Surveillance Computational modeling computer architecture Benchmark testing Transformers

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

引用

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

作者： Tonmoay Deb Lichen Wang Zachary Bessinger Naji Khosravan Eric Penner Sing Bing Kang Northwestern University Zillow Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper focuses on bridging the gap between natural language descriptions, 360° panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell 1 . We first introduce an effective technique for extracting geometric information from ZInD’s raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naïve methods in both tasks, hence, can be considered as a complement to the naïve to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： Geometry computer vision Shape Focusing Benchmark testing Information retrieval Human in the loop

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SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention

SAM-PM: Enhancing Video Camouflaged Object Detection using S...

引用

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

作者： Muhammad Nawfal Meeran Gokul Adethya T Bhanu Pratyush Mantha National Institute of Technology Tiruchirappalli

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the domain of large foundation models, the Segment Anything Model (SAM) has gained notable recognition for its exceptional performance in image segmentation. However, tackling the video camouflage object detection (VCOD) task presents a unique challenge. Camouflaged objects typically blend into the background, making them difficult to distinguish in still images. Additionally, ensuring temporal consistency in this context is a challenging problem. As a result, SAM encounters limitations and falls short when applied to the VCOD task. To overcome these challenges, we propose a new method called the SAM Propagation Module (SAM-PM). Our propagation module enforces temporal consistency within SAM by employing spatio-temporal cross-attention mechanisms. Moreover, we exclusively train the propagation module while keeping the SAM network weights frozen, allowing us to integrate task-specific insights with the vast knowledge accumulated by the large model. Our method effectively incorporates temporal consistency and domain-specific expertise into the segmentation network with an addition of less than 1% of SAM’s parameters. Extensive experimentation reveals a substantial performance improvement in the VCOD benchmark when compared to the most recent state-of-the-art techniques. Code and pre-trained weights are open-sourced at https://***/SpiderNitt/SAM-PM

关键词： Training Knowledge engineering Image segmentation computer vision Image recognition Codes conferences

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How Much You Ate? Food Portion Estimation on Spoons

How Much You Ate? Food Portion Estimation on Spoons

引用

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

作者： Aaryam Sharma Chris Czarnecki Yuhao Chen Pengcheng Xi Linlin Xu Alexander Wong Vision and Image Processing Lab University of Waterloo National Research Council Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil’s surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： computer vision Image segmentation Accuracy Pipelines Estimation Image capture Cameras

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Outsmarting Biometric Imposters: Enhancing Iris-recognition System Security through Physical Adversarial Example Generation and PAD Fine-Tuning

Outsmarting Biometric Imposters: Enhancing Iris-Recognition ...

引用

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

作者： Yuka Ogino Kazuya Kakizaki Takahiro Toizumi Atsushi Ito NEC Corporation

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the vulnerabilities of iris recognition systems to both image-based impersonation attacks and Presentation Attacks (PAs) in physical environments. While existing Presentation Attack Detection (PAD) methods have been effective against PAs, they remain susceptible to adversarial examples. We propose a combination of physical adversarial attacks tailored to iris recognition and PAD, and also propose a defense method against them. Our attack methods involve a physical impersonation attack using adversarial perturbation on the iris region and a physical PAD evading attack using an adversarial patch on the pupil region. We demonstrate the high transferability and effectiveness of our attacks on multiple PA instruments in digital and distinct physical environments using multiple recognition engines. To counteract these attacks, we develop a defense method for PAD involving adversarial fine-tuning against both the physical attacks. This defense method successfully reduces the PAD evasion attack success rate from 71.5% to 21.0% in physical environments and ultimately lowers the overall physical impersonation success rate from 58.0% to 19.5%. Our proposed method lays the groundwork for developing more robust and secure iris recognition systems with increased protection against sophisticated PAs.

关键词： Perturbation methods Instruments Impersonation attacks Robustness pattern recognition Security Pupils

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Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Fast and Accurate Quantized Camera Scene Detection on Smartp...

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

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Chen, Sheng Xia, Xin Liu, Zhaoyan Zhang, Yuwei Zhu, Feng Li, Jiashi Xiao, Xuefeng Tian, Yuan Wu, Xinglong Kyrkou, Christos Chen, Yixin Zhang, Zexin Peng, Yunbo Lin, Yue Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Kumar, Himanshu Ge, Chao Wu, Pei-Lin Du, Jin-Hua Batutin, Andrew Federico, Juan Pablo Lyda, Konrad Khojoyan, Levon Thanki, Abhishek Paul, Sayak Siddiqui, Shahid Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland AI Witchlabs Lausanne Switzerland ByteDance Inc Beijing Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Netease Games AI Lab Beijing Peoples R China Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India Indian Inst Technol Jodhpur Karwar India Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China DataArt Inc New York NY USA PyImageSearch Mumbai Maharashtra India Univ Cyprus KIOS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665448994

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Image analysis Computational modeling conferences Cameras Real-time systems

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ConvMLP: Hierarchical Convolutional MLPs for vision

ConvMLP: Hierarchical Convolutional MLPs for Vision

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

作者： Jiachen Li Ali Hassani Steven Walton Humphrey Shi SHI Lab @ University of Oregon & UIUC Picsart AI Research (PAIR)

MLP-based architectures, which consist of a sequence of consecutive multi-layer perceptron blocks, have recently been found to reach comparable results to convolutional and transformer-based methods on image classification. However, most methods adopt spatial MLPs which take fixed-dimension inputs, therefore making it difficult to apply them as backbones to downstream tasks such as object detection and semantic segmentation, which require inputs with arbitrary dimension. Moreover, single-stage designs further limit the performance in other computer vision tasks and fully-connected layers bear heavy computation. To tackle these problems, we propose ConvMLP: a Hierarchical Convolutional MLP for visual recognition, which is a lightweight, stage-wise, co-design of convolution layers, and MLPs. In particular, ConvMLP-S achieves 76.8% top-1 accuracy on ImageNet-1k with 9M parameters and 2.4 GMACs (15% and 19% of MLP-Mixer-B/16, respectively). Experiments on object detection and semantic segmentation further show that visual representation learned by ConvMLP can be seamlessly transferred to downstream tasks and achieve competitive results with fewer parameters. Our code and pre-trained models are open-sourced at https://***/SHI-Labs/Convolutional-MLPs.

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