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Is Neuron Coverage Needed to Make Person Detection More Robust?

Is Neuron Coverage Needed to Make Person Detection More Robu...

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

作者： Pavlitskaya, Svetlana Yikmis, Siyar Zoellner, J. Marius FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781665487399

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21% and 64.24% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.

关键词： Measurement Deep learning computer vision computer bugs Neurons Neural networks Training data

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Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation

Auxiliary Learning for Self-Supervised Video Representation ...

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

作者： Dadashzadeh, Amirhossein Whone, Alan Mirmehdi, Majid Univ Bristol Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the outstanding success of self-supervised pretraining methods for video representation learning, they generalise poorly when the unlabeled dataset for pretraining is small or the domain difference between unlabelled data in source task (pretraining) and labeled data in target task (finetuning) is significant. To mitigate these issues, we propose a novel approach to complement self-supervised pretraining via an auxiliary pretraining phase, based on knowledge similarity distillation, auxSKD, for better generalisation with a significantly smaller amount of video data, e.g. Kinetics-100 rather than Kinetics-400. Our method deploys a teacher network that iteratively distils its knowledge to the student model by capturing the similarity information between segments of unlabelled video data. The student model meanwhile solves a pretext task by exploiting this prior knowledge. We also introduce a novel pretext task, Video Segment Pace Prediction or VSPP, which requires our model to predict the playback speed of a randomly selected segment of the input video to provide more reliable self-supervised representations. Our experimental results show superior results to the state of the art on both UCF101 and HMDB51 datasets when pretraining on K100 in apple-to-apple comparisons. Additionally, we show that our auxiliary pretraining, auxSKD, when added as an extra pretraining phase to recent state of the art self-supervised methods (i.e. VCOP, VideoPace, and RSPNet), improves their results on UCF101 and HMDB51.

关键词： Representation learning Knowledge engineering Training Predictive models Data models pattern recognition Reliability

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Emphasizing Complementary Samples for Non-literal Cross-modal Retrieval

Emphasizing Complementary Samples for Non-literal Cross-moda...

引用

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

作者： Thomas, Christopher Kovashka, Adriana Columbia Univ New York NY 10027 USA Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing cross-modal retrieval methods assume a straightforward relationship where images and text contain portrayals or mentions of the same objects. In contrast, real-world image-text pairs (e.g. an image and its caption in a news article) often feature more complex relations. Importantly, not all image-text pairs have the same relationship: in some pairs, image and text may be more closely aligned, while others are more loosely aligned hence complementary. In order to ensure the model learns a semantically robust space which captures nuanced relationships, care must be taken that loosely-aligned image-text pairs have a strong enough impact on learning. In this paper, we propose a novel approach to prioritize loosely-aligned samples. Unlike prior sample weighting methods, ours relies on estimating to what extent semantic similarity is preserved in the separate channels (images/text) in the learned multimodal space. In particular, the image-text pair weights in the retrieval loss focus learning towards samples from diverse or discrepant neighborhoods: samples where images or text that were close in a semantic space, are distant in the cross-modal space (diversity), or where neighbor relations are asymmetric (discrepancy). Experiments on three challenging datasets exhibiting abstract image-text relations, as well as COCO, demonstrate significant performance gains compared to recent state-of-the-art models and sample weighting approaches.

关键词： computer vision Spatial diversity conferences Semantics Channel estimation Performance gain Benchmark testing

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Self-Supervised Normalizing Flows for Image Anomaly Detection and Localization

Self-Supervised Normalizing Flows for Image Anomaly Detectio...

引用

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

作者： Chiu, Li-Ling Lai, Shang-Hong National Tsing Hua University Department of Computer Science Taiwan

ISBN: (纸本)9798350302493

Image anomaly detection aims to detect out-of-distribution instances. Most existing methods treat anomaly detection as an unsupervised task because anomalous training data and labels are usually scarce or unavailable. Recently, image synthesis has been used to generate anomalous samples which deviate from normal sample distribution for model training. By using the synthesized anomalous training samples, we present a novel self-supervised normalizing flow-based density estimation model, which is trained by maximizing the likelihood of normal images and minimizing the likelihood of synthetic anomalous images. By adding constraints to abnormal samples in our loss function, our model training is focused on normal samples rather than synthetic samples. Moreover, we improve the transformation subnet of the affine coupling layers in our flow-based model by dynamic stacking convolution and self-attention blocks. We evaluate our method on MVTec-AD, BTAD, and DAGM datasets and achieve state-of-the-art performance compared to flow-based and self-supervised methods on both anomaly detection and localization tasks. © 2023 ieee.

关键词： Anomaly detection

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Trust Your IMU: Consequences of Ignoring the IMU Drift

Trust Your IMU: Consequences of Ignoring the IMU Drift

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

作者： Ornhag, Marcus Valtonen Persson, Patrik Wadenback, Marten Astrom, Kalle Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Linkoping Univ Dept Elect Engn Linkoping Sweden

ISBN: (纸本)9781665487399

In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model, which in turn admits further intrinsic calibration. We develop the first-ever solver to jointly solve the relative pose problem with unknown and equal focal length and radial distortion profile while utilizing the IMU data. Furthermore, we show significant speed-up compared to state-of-the-art algorithms, with small or negligible loss in accuracy for partially calibrated setups. The proposed algorithms are tested on both synthetic and real data, where the latter is focused on navigation using unmanned aerial vehicles (UAVs). We evaluate the proposed solvers on different commercially available low-cost UAVs, and demonstrate that the novel assumption on IMU drift is feasible in real-life applications. The extended intrinsic auto-calibration enables us to use distorted input images, making tedious calibration processes obsolete, compared to current state-of-the-art methods.

关键词： computer vision Measurement units Codes conferences Inertial navigation Distortion Real-time systems

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DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D vision

DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-bas...

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

作者： Ling, Lu Sheng, Yichen Tu, Zhi Zhao, Wentian Xin, Cheng Wan, Kun Yu, Lantao Guo, Qianyu Yu, Zixun Lu, Yawen Li, Xuanmao Sun, Xingpeng Ashok, Rohan Mukherjee, Aniruddha Kang, Hao Kong, Xiangrui Hua, Gang Zhang, Tianyi Benes, Bedrich Bera, Aniket Purdue Univ W Lafayette IN 47907 USA Adobe Inc San Jose CA USA Rutgers State Univ New Brunswick NJ USA Google Inc Mountain View CA USA Huazhong Univ Sci & Technol Wuhan Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9798350353006

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to either synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point-of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible.

关键词： Contrastive Learning

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Robust Automatic Motorcycle Helmet Violation Detection for an Intelligent Transportation System

Robust Automatic Motorcycle Helmet Violation Detection for a...

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

作者： Tran, Duong Nguyen-Ngoc Hoang Pham, Long Jeon, Hyung-Joon Nguyen, Huy-Hung Jeon, Hyung-Min Tran, Tai Huu-Phuong Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350302493

Video surveillance-based automatic detection of motorcycle helmet usage can enhance the effectiveness of educational and enforcement initiatives aimed at boosting road safety. Current detection methods, however, have room for enhancement, such as the inability to pinpoint individual motorcycles or differentiate between drivers and passengers in terms of helmet usage. This paper introduces a framework designed to detect and identify individual motorcycles while recording specific helmet usage for riders. The proposed classification approach for helmet usage demonstrates increased efficiency in comparison to previous research. Our findings highlight the exceptional accuracy of deep learning, with our method achieving a score of 0.7754 on the AI City 2023 Challenge Track 5 public leaderboard. © 2023 ieee.

关键词： Motorcycles

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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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Simple and Efficient Architectures for Semantic Segmentation

Simple and Efficient Architectures for Semantic Segmentation

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

作者： Mehta, Dushyant Skliar, Andrii Ben Yahia, Haitam Borse, Shubhankar Porikli, Fatih Habibian, Amirhossein Blankevoort, Tijmen Qualcomm AI Res San Diego CA 92121 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use of operations that are inefficient on current hardware. This paper demonstrates that a simple encoder-decoder architecture with a ResNet-like backbone and a small multi-scale head, performs on-par or better than complex semantic segmentation architectures such as HRNet, FANet and DDRNets. Naively applying deep backbones designed for Image Classification to the task of Semantic Segmentation leads to sub-par results, owing to a much smaller effective receptive field of these backbones. Implicit among the various design choices put forth in works like HRNet, DDRNet, and FANet are networks with a large effective receptive field. It is natural to ask if a simple encoder-decoder architecture would compare favorably if comprised of backbones that have a larger effective receptive field, though without the use of inefficient operations like dilated convolutions. We show that with minor and inexpensive modifications to ResNets, enlarging the receptive field, very simple and competitive baselines can be created for Semantic Segmentation. We present a family of such simple architectures for desktop as well as mobile targets, which match or exceed the performance of complex models on the Cityscapes dataset. We hope that our work provides simple yet effective baselines for practitioners to develop efficient semantic segmentation models. The model definitions and pre-trained weights are available at https://***/Qualcomm-AI-research/FFNet.

关键词： Image segmentation Head Semantics Graphics processing units computer architecture Hardware Distance measurement

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MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

MARRS: Modern Backbones Assisted Co-training for Rapid and R...

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

作者： Jain, Saurabh Kumar Das, Sukhendu Indian Institute of Technology Visualization and Perception Lab Department of Computer Science Engineering Madras India

ISBN: (纸本)9798350302493

Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction with ResNet34 backbone, which makes them overlook the advantages of utilizing other backbones. Hence, in this paper, we investigate the impact of employing different modern backbones in SSDA and propose a novel solution named Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation (MARRS), that uses discriminative features of two modern backbones for training linear classifiers using the well established co-training framework. To induce diversity among classifiers for effective co-training, we propose a novel module that produces diversity at three levels, namely image, backbone, and feature distribution levels. Experiments reveal that MARRS not only achieves state-of-the-art performance across all popular SSDA datasets, but also drastically cuts the computation time compared to other SSDA approaches, making MARRS a rapid and robust solution for SSDA. We also provide extensive ablation experiments to verify our framework's vitality and primary design choices. © 2023 ieee.

关键词： computer vision

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