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Sensor Equivariance: A Framework for Semantic Segmentation with Diverse Camera Models

Sensor Equivariance: A Framework for Semantic Segmentation w...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hannes Reichert Manuel Hetzel Andreas Hubert Konrad Doll Bernhard Sick University of Applied Sciences Aschaffenburg Germany University of Kassel Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Objects are represented differently in projection-based sensors such as cameras depending on sensor resolution, field of view, and distortion, leading to distorted physical and geometric properties. As a result, sensor data processing depend on these properties. With the large variations of sensors on the market, an equivariant representation and suitable processing are necessary to become independent of the sensor used. In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. Furthermore, we introduce a SensorConv layer as an extension to the conventional convolution layer. SensorConv enable using projection properties in convolutional neural networks. To that end, we propose an architecture for using the SensorConv layer in the Detectron2 [21] framework. We collected a dataset of equirectangular images for our experiments with the CARLA [3] simulator. To analyze multiple sensor models (i.e., sensor intrinsic), we created an augmentation method to emulate a high variability of sensors from the collected equirectangular panoramas. In our experiment, we show that our method can generalize better across different camera sensors.

关键词： computer vision Convolution Semantic segmentation conferences Cameras Distortion Data processing

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MobileViG: Graph-Based Sparse Attention for Mobile vision Applications

MobileViG: Graph-Based Sparse Attention for Mobile Vision Ap...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mustafa Munir William Avery Radu Marculescu The University of Texas at Austin

Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provide a new avenue for exploration. Unfortunately, for mobile applications, ViGs are computationally expensive due to the overhead of representing images as graph structures. In this work, we propose a new graph-based sparse attention mechanism, Sparse vision Graph Attention (SVGA), that is designed for ViGs running on mobile devices. Additionally, we propose the first hybrid CNN-GNN architecture for vision tasks on mobile devices, MobileViG, which uses SVGA. Extensive experiments show that MobileViG beats existing ViG models and existing mobile CNN and ViT architectures in terms of accuracy and/or speed on image classification, object detection, and instance segmentation tasks. Our fastest model, MobileViG-Ti, achieves 75.7% top-1 accuracy on ImageNet-1K with 0.78 ms inference latency on iPhone 13 Mini NPU (compiled with CoreML), which is faster than MobileNetV2x1.4 (1.02 ms, 74.7% top-1) and MobileNetV2x1.0 (0.81 ms, 71.8% top-1). Our largest model, MobileViG-B obtains 82.6% top-1 accuracy with only 2.30 ms latency, which is faster and more accurate than the similarly sized EfficientFormer-L3 model (2.77 ms, 82.4%). Our work proves that well designed hybrid CNN-GNN architectures can be a new avenue of exploration for designing models that are extremely fast and accurate on mobile devices. Our code is publicly available at https://***/SLDGroup/MobileViG.

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AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

AIS 2024 Challenge on Video Quality Assessment of User-Gener...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcos V. Conde Saman Zadtootaghaj Nabajeet Barman Radu Timofte Chenlong He Qi Zheng Ruoxi Zhu Zhengzhong Tu Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Zicheng Zhang Haoning Wu Yingjie Zhou Chunyi Li Xiaohong Liu Weisi Lin Guangtao Zhai Wei Sun Yuqin Cao Yanwei Jiang Jun Jia Zhichao Zhang Zijian Chen Weixia Zhang Xiongkuo Min Steve Göring Zihao Qi Chen Feng CAIDAS & IFI Computer Vision Lab. University of Würzburg Sony Interactive Entertainment FTG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted results during the challenge period. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for Video Quality Assessment of user-generated content.

关键词： Surveys Learning systems Video on demand Reviews User-generated content Quality assessment pattern recognition

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Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble

Divide and Conquer: High-Resolution Industrial Anomaly Detec...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Blaž Rolih Dick Ameln Ashwin Vaidya Samet Akcay Faculty of Computer and Information Science University of Ljubljana Intel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a high resolution. This frequently poses significant challenges concerning memory consumption during the model training and inference stages, leaving some existing methods impractical for widespread adoption. To overcome this challenge, we present the tiled ensemble approach, which reduces memory consumption by dividing the input images into a grid of tiles and training a dedicated model for each tile location. The tiled ensemble is compatible with any existing anomaly detection model without the need for any modification of the underlying architecture. By introducing overlapping tiles, we utilize the benefits of traditional stacking ensembles, leading to further improvements in anomaly detection capabilities beyond high resolution alone. We perform a comprehensive analysis using diverse underlying architectures, including Padim, PatchCore, FastFlow, and Reverse Distillation, on two standard anomaly detection datasets: MVTec and VisA. Our method demonstrates a notable improvement across setups while remaining within GPU memory constraints, consuming only as much GPU memory as a single model needs to process a single tile. 1 2

关键词： Training computer vision Image resolution Tiles Memory management Stacking Graphics processing units

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Fully Test-time Adaptation for Object Detection

Fully Test-time Adaptation for Object Detection

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaoqian Ruan Wei Tang University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though the object detection performance on standard benchmarks has been improved drastically in the last decade, current object detectors are often vulnerable to domain shift between the training data and testing images. Domain adaptation techniques have been developed to adapt an object detector trained in a source domain to a target domain. However, they assume that the target domain is known and fixed and that a target dataset is available for training, which cannot be satisfied in many real-world applications. To close this gap, this paper investigates fully test-time adaptation for object detection. It means to update a trained object detector on a single testing image before making a prediction, without access to the training data. Through a diagnostic study of a baseline self-training framework, we show that a great challenge of this task is the unreliability of pseudo labels caused by domain shift. We then propose a simple yet effective method, termed the IoU Filter, to address this challenge. It consists of two new IoU-based indicators, both of which are complementary to the detection confidence. Experimental results on five datasets demonstrate that our approach could effectively adapt a trained detector to various kinds of domain shifts at test time and bring substantial performance gains. Code is available at https://***/XiaoqianRuan1/IoU-filter.

关键词： Training computer vision Codes conferences Training data Detectors Object detection

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Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey

Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Sur...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nicolas Chahine Marcos V. Conde Daniela Carfora Gabriel Pacianotto Benoit Pochon Sira Ferradans Radu Timofte Zhichao Duan Xinrui Xu Yipo Huang Quan Yuan Xiangfei Sheng Zhichao Yang Leida Li Haotian Fan Fangyuan Kong Yifang Xu Wei Sun Weixia Zhang Yanwei Jiang Haoning Wu Zicheng Zhang Jun Jia Yingjie Zhou Zhongpeng Ji Xiongkuo Min Weisi Lin Guangtao Zhai Xiaoqi Wang Junqi Liu Zixi Guo Yun Zhang Zewen Chen Wen Wang Juan Wang Bing Li DXOMARK CAIDAS & IFI Computer Vision Lab University of Würzburg Xidian University ByteDance Inc Shanghai Jiao Tong University Nanyang Technological University Huawei School of Electronics and Communication Engineering Sun Yat-sen University China State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Jiaotong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results. This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos. The methods must generalize to diverse scenes and diverse lighting conditions (indoor, outdoor, low-light), movement, blur, and other challenging conditions. In the challenge, 140 participants registered, and 35 submitted results during the challenge period. The performance of the top 5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in Portrait Quality Assessment.

关键词： Surveys computer vision Reviews conferences Lighting Artificial neural networks Quality assessment pattern recognition

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

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

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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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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zane K. J. Hartley Rob J. Lind Michael P. Pound Andrew P. French University of Nottingham Syngenta Jealott’s Hill International Research Centre

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Training Instance segmentation computer vision Costs Generative AI conferences Pipelines

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Prompting Foundational Models for Omni-supervised Instance Segmentation

Prompting Foundational Models for Omni-supervised Instance S...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Arnav M. Das Ritwick Chaudhry Kaustav Kundu Davide Modolo University of Washington Seattle AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Pixel-level mask annotation costs are a major bottleneck in training deep neural networks for instance segmentation. Recent promptable foundation models like the Segment Anything Model (SAM) and GroundedDINO (GDino) have shown impressive zero-shot performance in segmentation and object detection benchmarks. While these models are not capable of performing inference without prompts, they are ideal for omnisupervised learning, where weak labels are used to derive supervisory signals for complex tasks. In our work, we use SAM and GDino as teacher models and prompt them with weak annotations to create high-quality pseudomasks. These pseudomasks are then used to train student instance segmentation models, which do not require prompts at inference time. We explore various weak annotations, such as bounding boxes, points, and image-level class labels, and show that a student model can achieve roughly 95% of a fully-supervised model’s performance while reducing annotation costs by 7×. We show the effectiveness of our approach on challenging instance segmentation benchmarks such as COCO [15], ADE20K [30], Cityscapes [9]. Our approach can be used to reduce annotation cost to train instance segmentation models, making it more accessible to a wider range of applications.

关键词： Instance segmentation Training computer vision Costs Annotations Computational modeling conferences

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FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks

FewSOME: One-Class Few Shot Anomaly Detection with Siamese N...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Niamh Belton Misgina Tsighe Hagos Aonghus Lawlor Kathleen M. Curran Science Foundation Ireland Centre for Research Training in Machine Learning School of Medicine University College Dublin School of Computer Science University College Dublin Insight Centre for Data Analytics University College Dublin Dublin Ireland

Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples are available for training. We propose ‘Few Shot anOMaly dEtection’ (FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to accurately detect anomalies having trained on ‘few’ examples of the normal class and no examples of the anomalous class. We describe FewSOME to be of low complexity given its low data requirement and short training time. FewSOME is aided by pretrained weights with an architecture based on Siamese Networks. By means of an ablation study, we demonstrate how our proposed loss, ‘Stop Loss’, improves the robustness of FewSOME. Our experiments demonstrate that FewSOME performs at stateof-the-art level on benchmark datasets MNIST, CIFAR-10, F-MNIST and MVTec AD while training on only 30 normal samples, a minute fraction of the data that existing methods are trained on. Moreover, our experiments show FewSOME to be robust to contaminated datasets. We also report F1 score and balanced accuracy in addition to AUC as a benchmark for future techniques to be compared against. Code available; https://***/niamhbelton/FewSOME.

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