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The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study

The Effect of Improving Annotation Quality on Object Detecti...

引用

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

作者： Ma, Jiaxin Ushiku, Yoshitaka Sagara, Miori OMRON SINIC X Corp Tokyo Japan Baobab Inc Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this study, we partially reannotate conventional benchmark datasets for object detection and check whether there is performance improvement/drop compared with the original annotations. Recent studies on the annotation qualities of ImageNet for image classification revealed some issues of how to associate only a single label to each image accurately. Object detection, on the other hand, should have other nontrivial issues because there are multiple objects in a single image, and realizing consistency among bounding boxes is challenging. A team of professional annotators was formed for MS COCO and Google Open Images datasets. To realize highly-consistent annotations, we prepared carefully designed guidelines for each category and selected quality inspectors who checked the annotation quality of each annotator. Finally, we applied conventional object detection methods for reannotated parts of each dataset. We found mixed results: whether the performance dropped or improved depended on each category and dataset.

关键词： Annotations conferences Object detection Machine learning Benchmark testing Internet pattern recognition

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Multiple Object Detection and Tracking in the Thermal Spectrum

Multiple Object Detection and Tracking in the Thermal Spectr...

引用

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

作者： El Ahmar, Wassim A. Kolhatkar, Dhanvin Nowruzi, Farzan Erlik AlGhamdi, Hamzah Hou, Jonathan Laganiere, Robert Univ Ottawa Ottawa ON Canada Sensor Cortek Inc Ottawa ON Canada Pleora Technol Kanata ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple Object Tracking (MOT) is an integral part of machine vision research. Most tracking-by-detection based MOT solutions utilize video streams from RGB cameras for their operation. However, for real-world applications, it is necessary to utilize sensors that operate in different spectrums to accommodate for varying lighting conditions. Since object detection is the first step of the tracking pipeline in tracking-by-detection approaches, we compare the performance of state-of-the-art object detectors when trained on color images to their performance when trained on thermal images. We introduce a new dataset for multiple object tracking with thermal images and corresponding RGB images and show that state-of-the-art trackers perform better on thermal images, especially in poor lighting conditions. Finally, we propose the use of a dynamic cut-off threshold for tracking-by-detection approaches that factors the size of a predicted box to enhance the tracker association. Our dataset and source code are publicly available at https://***/wassimea/thermalMOT

关键词： Machine vision conferences Pipelines Lighting Object detection Detectors Streaming media

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Wearable ImageNet: Synthesizing Tileable Textures via Dataset Distillation

Wearable ImageNet: Synthesizing Tileable Textures via Datase...

引用

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent methods for Dataset Distillation are able to take in a large set of images of a specific class (e.g., from ImageNet) and synthesize a single image, such that a classifier trained on that image could perform similarly to one trained on the original dataset. It was noticed that the resulting "distilled images" are often quite visually pleasing. In this paper, we describe a simple method for generating tileable distilled textures by sampling random crops from a toroidal canvas of synthetic pixels while enforcing that all such crops serve as effective distilled training data. Such distilled textures not only summarize a given image category in a visually interesting way, but also allow for generation of infinite texture patterns suitable for printing on fabric, clothing, etc. This paper might be just the first step in making the ImageNet dataset into a fashion statement.

关键词： Printing computer vision Wearable computers conferences Clothing Crops Training data

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Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

引用

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

作者： Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781665487399

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the down-sampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

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Neural Fourier Filter Bank

Neural Fourier Filter Bank

引用

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

作者： Wu, Zhijie Jin, Yuhe Yi, Kwang Moo Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://***/ubc-vision/NFFB.

关键词： vision + graphics

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Poisons that are learned faster are more effective

Poisons that are learned faster are more effective

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

作者： Sandoval-Segura, Pedro Singla, Vasu Fowl, Liam Geiping, Jonas Goldblum, Micah Jacobs, David Goldstein, Tom Univ Maryland College Pk MD 20742 USA New York Univ New York NY USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we benchmark error-minimizing, error-maximizing, and synthetic poisons in terms of peak test accuracy over 100 epochs and make a number of surprising observations. First, we find that poisons that reach a low training loss faster have lower peak test accuracy. Second, we find that a current state-of-the-art error-maximizing poison is 7x less effective when poison training is stopped at epoch 8. Third, we find that stronger, more transferable adversarial attacks do not make stronger poisons. We advocate for evaluating poisons in terms of peak test accuracy.

关键词： Training Data privacy Privacy computer vision Toxicology Correlation Perturbation methods

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Is synthetic voice detection research going into the right direction?

Is synthetic voice detection research going into the right d...

引用

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

作者： Borzi, Stefano Giudice, Oliver Stanco, Filippo Allegra, Dario Univ Catania Dept Math & Comp Sci Catania Italy Banca Italia IT Dept Appl Res Team Rome Italy

ISBN: (纸本)9781665487399

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.

关键词： Deep learning Training computer vision Image color analysis Forensics conferences Bit rate

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Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image recognition

Incremental Meta-Learning via Episodic Replay Distillation f...

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

作者： Wang, Kai Liu, Xialei Bagdanov, Andy Herranz, Luis Jui, Shangling van de Weijer, Joost Comp Vis Ctr Barcelona Spain Nankai Univ Tianjin Peoples R China Univ Florence MICC Florence Italy Huawei Kirin Solut Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more generalizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively.

关键词： Training computer vision Image recognition Upper bound conferences pattern recognition Task analysis

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Less is More: Proxy Datasets in NAS approaches

Less is More: Proxy Datasets in NAS approaches

引用

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

作者： Moser, Brian Raue, Federico Hees, Joern Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Darmstadt Germany TU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.

关键词： Training computer vision conferences Neural networks Training data computer architecture Search problems

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Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

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

作者： Modi, Rajat Rana, Aayush Jung Kumar, Akash Tirupattur, Praveen Vyas, Shruti Rawat, Yogesh Singh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665487399

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

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