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SIDOD: A Synthetic Image Dataset for 3D Object Pose recognition with Distractors 32

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognit...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jalal, Mona Spjut, Josef Boudaoud, Ben Betke, Margrit Boston Univ Boston MA 02215 USA NVIDIA Raleigh NC USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.

关键词： Three-dimensional displays Pose estimation Cameras Image segmentation computer vision Training Lighting

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Deep Learning for Automatic Pneumonia Detection

Deep Learning for Automatic Pneumonia Detection

引用

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

作者： Gabruseva, Tatiana Poplavskiy, Dmytro Kalinin, Alexandr Topcon Positioning Syst Brisbane Qld Australia Univ Michigan Ann Arbor MI 48109 USA Shenzhen Res Inst Big Data Shenzhen 518172 Guangdong Peoples R China

ISBN: (纸本)9781728193601

Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. The pneumonia detection is usually performed through examine of chest X-Ray radiograph by highly-trained specialists. This process is tedious and often leads to a disagreement between radiologists. computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-extinction deep convolution neural networks, augmentations and multi-task learning. The proposed approach was evaluated in the context of the Radiological Society of North America Pneumonia Detection Challenge, achieving one of the best results in the challenge.

关键词： computer aided diagnosis

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ViTKD: Feature-based Knowledge Distillation for vision Transformers

ViTKD: Feature-based Knowledge Distillation for Vision Trans...

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

作者： Yang, Zhendong Li, Zhe Zeng, Ailing Li, Zexian Yu, Chun Yu, Liming Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Beihang Univ Beijing Peoples R China IDEA Shenzhen Peoples R China

ISBN: (纸本)9798350365474

Knowledge Distillation (KD) has been extensively studied as a means to enhance the performance of smaller models in Convolutional Neural Networks (CNNs). Recently, the vision Transformer (ViT) has demonstrated remarkable success in various computer vision tasks, leading to an increased demand for KD in ViT. However, while logit-based KD has been applied to ViT, other feature-based KD methods for CNNs cannot be directly implemented due to the significant structure gap. In this paper, we conduct an analysis of the properties of different feature layers in ViT to identify a method for feature-based ViT distillation. Our findings reveal that both shallow and deep layers in ViT are equally important for distillation and require distinct distillation strategies. Based on these guidelines, we propose our feature-based method ViTKD, which mimics the shallow layers and generates the deep layer in the teacher. ViTKD leads to consistent and significant improvements in the students. On ImageNet-1K, we achieve performance boosts of 1.64% for DeiT-Tiny, 1.40% for DeiT-Small, and 1.70% for DeiT-Base. Downstream tasks also demonstrate the superiority of ViTKD. Additionally, ViTKD and logit-based KD are complementary and can be applied together directly, further enhancing the student's performance. Specifically, DeiT-T, S, and B achieve accuracies of 77.78%, 83.59%, and 85.41%, respectively, using this combined approach. Code is available at https:// github. com/ yzdv/cls_KD.

关键词： Convolutional neural networks

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Zero-shot Learning Using Multimodal Descriptions

Zero-shot Learning Using Multimodal Descriptions

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

作者： Mall, Utkarsh Hariharan, Bharath Bala, Kavita Cornell Univ Ithaca NY 14853 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Zero-shot learning (ZSL) tackles the problem of recognizing unseen classes using only semantic descriptions, e.g., attributes. Current zero-shot learning techniques all assume that a single vector of attributes suffices to describe each category. We show that this assumption is incorrect. Many classes in real world problems have multiple modes of appearance: male and female birds vary in appearance, for instance. Domain experts know this and can provide attribute descriptions of the chief modes of appearance for each class. Motivated by this, we propose the task of multi-modal zero-shot learning, where the learner must learn from these multimodal attribute descriptions. We present a technique for addressing this problem of multimodal ZSL that outperforms the unimodal counterpart significantly. We posit that multimodal ZSL is more practical for real-world problems where complex intra-class variation is common.

关键词： computer vision conferences Computational modeling Semantics Manuals Benchmark testing Birds

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Neurodata Lab's approach to the Challenge on computer vision for Physiological Measurement

Neurodata Lab's approach to the Challenge on Computer Vision...

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

作者： Artemyev, Mikhail Churikova, Marina Grinenko, Mikhail Perepelkina, Olga Neurodata Lab LLC Miami FL 33137 USA Lomonosov Moscow State Univ Fac Biol Dept Higher Nervous Act Moscow Russia

ISBN: (纸本)9781728193601

This paper introduces the Neurodata Lab's approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within CVPR2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computer vision. Our deep learning-based approach includes 3D spatio-temporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).

关键词： computer vision

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Perceptual in-Loop Filter for Image and Video Compression

Perceptual in-Loop Filter for Image and Video Compression

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

作者： Wang, Huairui Ren, Guangjie Ouyang, Tong Zhang, Junxi Han, Wenwei Liu, Zizheng Chen, Zhenzhong Wuhan Univ Wuhan Peoples R China Tencent Media Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we introduce our hybrid image and video compression scheme enhanced by CNN-optimized in-loop filter. Specifically, a Structure Preserving in-Loop Filter (SPiLF) is incorporated in the hybrid video codec Enhanced Compression Model (ECM), where two branches, i.e., gradient branch and pixel branch, are developed based on the dense residual unit (DRU). To provide pleasant visual quality, the Generative adversarial networks (GAN) loss and LPIPS loss are further considered. Therefore, the proposal is mainly focusing on perceptual-friendly image compression for human vision, whilst video compression could be further investigated. The experiments show that the proposed method achieves advanced visual quality when compared to the traditional methods.

关键词： Visualization computer vision Image coding conferences Focusing Video compression Generative adversarial networks

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Multi-task Learning with Attention for End-to-end Autonomous Driving

Multi-task Learning with Attention for End-to-end Autonomous...

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

作者： Ishihara, Keishi Kanervisto, Anssi Miura, Jun Hautamaki, Ville Toyohashi Univ Technol Toyohashi Aichi Japan Univ Eastern Finland Kuopio Finland

ISBN: (纸本)9781665448994

Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable performance in point-to-point navigational scenarios, using a realistic simulator and standard benchmarks. Offline imitation learning is readily available, as it does not require expensive hand annotation or interaction with the target environment, but it is difficult to obtain a reliable system. In addition, existing methods have not specifically addressed the learning of reaction for traffic lights, which are a rare occurrence in the training datasets. Inspired by the previous work on multi-task learning and attention modeling, we propose a novel multi-task attention-aware network in the conditional imitation learning (CIL) framework. This does not only improve the success rate of standard benchmarks, but also the ability to react to traffic lights, which we show with standard benchmarks.

关键词： Training Visualization Benchmark testing pattern recognition Automobiles Reliability Task analysis

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Multi-Class Cell Detection Using Modified Self-Attention

Multi-Class Cell Detection Using Modified Self-Attention

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

作者： Sugimoto, Tatsuhiko Ito, Hiroaki Teramoto, Yuki Yoshizawa, Akihiko Bise, Ryoma Kyushu Univ Fukuoka Japan Kyoto Univ Hosp Kyoto Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-class cell detection (cancer or non-cancer) from a whole slide image (WSI) is an important task for pathological diagnosis. Cancer and non-cancer cells often have a similar appearance, so it is difficult even for experts to classify a cell from a patch image of individual cells. They usually identify the cell type not only on the basis of the appearance of a single cell but also on the context of the surrounding cells. For using such information, we propose a multi-class cell-detection method that introduces a modified self-attention to aggregate the surrounding image features of both classes. Experimental results demonstrate the effectiveness of the proposed method;our method achieved the best performance compared with a method, which simply uses the standard self-attention method.

关键词： Heating systems Pathology computer vision Aggregates conferences Feature extraction pattern recognition

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The Role of Shape for Domain Generalization on Sparsely-Textured Images

The Role of Shape for Domain Generalization on Sparsely-Text...

引用

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

作者： Nazari, Narges Honarvar Kovashka, Adriana Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

State-of-the-art object recognition methods do not generalize well to unseen domains. Work in domain generalization has attempted to bridge domains by increasing feature compatibility, but has focused on standard, appearance-based representations. We show the potential of shape-based representations to increase domain robustness. We compare two types of shape-based representations: one trains a convolutional network over edge features, and another computes a soft, dense medial axis transform. We show the complementary strengths of these representations for different types of domains, and the effect of the amount of texture that is preserved. We show that our shape-based techniques better leverage data augmentations for domain generalization, and are more effective at texture bias mitigation than shape-inducing augmentations. Finally, we show that when the convolutional network in state-of-the-art domain generalization methods is replaced with one that explicitly captures shape, we obtain improved results.

关键词： Bridges computer vision Shape conferences Transforms Robustness pattern recognition

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Beyond the Screen: Evaluating Deepfake Detectors under Moire pattern Effects

Beyond the Screen: Evaluating Deepfake Detectors under Moire...

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

作者： Tariq, Razaib Heo, Minji Woo, Simon S. Tariq, Shahroz Sungkyunkwan Univ Seoul South Korea CSIROs Data61 Eveleigh Australia

ISBN: (纸本)9798350365474

The detection of deepfakes is crucial for mitigating the societal impact of falsified video content. Despite the development of various algorithms for this purpose, challenges arise for detectors in real-world scenarios, especially when users capture deepfake content from screens and upload it online or when detectors operate on external devices like smartphones, requiring the capture of potential deepfakes through the camera for evaluation. A significant challenge in these scenarios is the presence of Moire patterns, which degrade image quality and complicate conventional classification methods, notably deep neural networks (DNNs). However, the impact of Moire patterns on the effectiveness of deepfake detection systems has not been adequately explored. This study aims to investigate how capturing deepfake videos via digital screen cameras affects the accuracy of detection mechanisms. We introduced the Moire patterns by capturing the display of a monitor using a smartphone camera and conducted empirical evaluations using four widely recognized datasets: CelebDF, DFD, DFDC, and FF++. We compare the performance of twelve SOTA detectors on deepfake videos captured under the influence of Moire patterns. Our findings reveal a performance decrease of up to 33.1 and 31.3 percentage points for image- and video-based detectors. Therefore, highlighting the challenges posed by Moire patterns and other naturally induced artifacts is critical for improving the effectiveness of real-world deepfake detection efforts. To facilitate further research, we will release the Moire pattern impact version of CelebDF, DFD, DFDC, and FF++ datasets with this paper. Our code is available here: https: //***/Razaib-Tariq/deepmoire

关键词： Deepfake Fake Media Media Forensics Moire pattern

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