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Hybrid Message Passing with Performance-Driven Structures for Facial Action Unit Detection

Hybrid Message Passing with Performance-Driven Structures fo...

引用

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

作者： Song, Tengfei Cui, Zijun Zheng, Wenming Ji, Qiang Southeast Univ Nanjing Peoples R China Rensselaer Polytech Inst Troy NY 12181 USA

ISBN: (纸本)9781665445092

Message passing neural network has been an effective method to represent dependencies among nodes by propagating messages. However, most of message passing algorithms focus on one structure and messages are estimated by one single approach. For real-world data, like facial action units (AUs), the dependencies may vary in terms of different expressions and individuals. In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner. Particularly, a performance-driven Monte Carlo Markov Chain sampling method is proposed for generating high performance graph structures. Besides, hybrid message passing is proposed to combine different types of messages, which provide the complementary information. The contribution of each type of message is adaptively adjusted along with different inputs. The experiments on two widely used benchmark datasets, i.e., BP4D and DISFA, validate that our proposed method can achieve the state-of-the-art performance.

关键词： Gold computer vision Monte Carlo methods Databases Message passing Neural networks Markov processes

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Scaling Local Self-Attention for Parameter Efficient Visual Backbones

Scaling Local Self-Attention for Parameter Efficient Visual ...

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

作者： Vaswani, Ashish Ramachandran, Prajit Srinivas, Aravind Parmar, Niki Hechtman, Blake Shlens, Jonathon Google Res Mountain View CA 94043 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutions.(1)

关键词： computer vision Visualization Image segmentation Computational modeling Transfer learning Memory management Object detection

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Robust Data Augmentation and Ensemble Method for Object Detection in Fisheye Camera Images

Robust Data Augmentation and Ensemble Method for Object Dete...

引用

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

作者： Viet Hung Duong Duc Quyen Nguyen Thien Van Luong Huan Vu Tien Cuong Nguyen VNPT Group VNPT AI Hanoi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, traffic surveillance systems have begun leveraging fisheye lenses to minimize the requisite number of cameras for comprehensive coverage of streets and intersections. However, as fisheye images have large radial distortion, they pose new challenges to standard object detection algorithms. In this study, we propose a robust object detection method in traffic scenarios using fisheye cameras. Specifically, we develop a novel data augmentation method, which is applied to VisDrone dataset. Note that we select this dataset for augmentation, since it bears resemblances to the Fisheye8K dataset. Furthermore, we leverage pseudo labels generated by a pre-trained object detection model based on the Fisheye8K and original VisDrone dataset to further enrich the training data. Finally, we utilize various state-of-the-art object detection models trained with different combinations of the proposed augmented data, which are then combined with robust ensemble techniques to further enhance the overall object detection performance. As a result, our proposed method achieves a final F1 score of 64.06% on the 2024 AI City Challenge - Track 4 and ranks first among the competing teams.

关键词： Surveillance Urban areas Training data Object detection Traffic control Data augmentation Cameras

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Public Life in Public Space (PLPS): A multi-task, multi-group video dataset for public life research 18

Public Life in Public Space (PLPS): A multi-task, multi-grou...

引用

ieee/cvf International conference on computer vision (ICCVW)

作者： Qing, Linbo Li, Lindong Xu, Shengyu Huang, Yibo Liu, Mei Jin, Rulong Liu, Bo Niu, Tong Wen, Hongqian Wang, Yuchen Jiang, Xue Peng, Yonghong Sichuan Univ Chengdu Peoples R China Manchester Metropolitan Univ Manchester Lancs England

ISBN: (纸本)9781665401913

Human-computer interaction (HCI) is a multidisciplinary field of study focusing on the design of computer technology and, in particular, the interactions between humans and computers. Public space, between the urban buildings, is an open and accessible area to people. Public life, happening in public spaces, is about human activity, human interaction, expression of human feeling in the wild. Affective behavior analysis in the public space is the basic topic of the public life research, which is the key to achieve HCI applications through comprehensively understanding people's feelings, emotions, social behaviors and their correlations in a 'human-centered' and engaging manner. However, it is a challenging task to design a robust HCI system due to the lack of multi-task datasets (including emotion, behavior, social relations, etc), collected under the uncontrolled conditions in real public spaces. In spite that existing separate datasets in computer vision can somehow meet the requirement of public life research, they are neither captured from real public spaces nor for multiple tasks, which cannot comprehensively support the joint research of public life. To tackle this issue, this paper presents a multi-task, multi-group human-oriented video dataset, namely public life in public space (PLPS). Specifically, multi-tasks in terms of activity recognition, emotion recognition and social relation recognition are integrated for each video data. Multi-group and multi-level labels in terms of individuals, groups, video clips are included in the dataset. With PLPS, more sophisticated computer vision model for comprehensive public life research can be facilitated.

关键词： Human computer interaction computer vision Emotion recognition Correlation conferences Computational modeling Buildings

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HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features

HoHoNet: 360 Indoor Holistic Understanding with Latent Horiz...

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

作者： Sun, Cheng Sun, Min Chen, Hwann-Tzong Natl Tsing Hua Univ Hsinchu Taiwan ASUS AICS Dept Singapore Singapore Joint Res Ctr AI Technol & All Vista Healthcare Taipei Taiwan Aeolus Robot San Francisco CA USA

ISBN: (纸本)9781665445092

We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHoNet advances in two important aspects. First, the deep architecture is redesigned to run faster with improved accuracy. Second, we propose a novel horizon-to-dense module, which relaxes the per-column output shape constraint, allowing per-pixel dense prediction from LHFeat. HoHoNet is fast: It runs at 52 FPS and 110 FPS with ResNet-50 and ResNet-34 backbones respectively, for modeling dense modalities from a high-resolution 512 x 1024 panorama. HoHoNet is also accurate. On the tasks of layout estimation and semantic segmentation, HoHoNet achieves results on par with current state-of-the-art. On dense depth estimation, HoHoNet outperforms all the prior arts by a large margin.

关键词： Image segmentation computer vision Shape Layout Semantics Estimation Deep architecture

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Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid ...

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

作者： Wang Yifan Wu, Shihao Oztireli, Cengiz Sorkine-Hornung, Olga Swiss Fed Inst Technol Zurich Switzerland Univ Cambridge Cambridge England

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Neural implicit functions have emerged as a powerful representation for surfaces in 3D. Such a function can encode a high quality surface with intricate details into the parameters of a deep neural network. However, optimizing for the parameters for accurate and robust reconstructions remains a challenge, especially when the input data is noisy or incomplete. In this work, we develop a hybrid neural surface representation that allows us to impose geometry-aware sampling and regularization, which significantly improves the fidelity of reconstructions. We propose to use iso-points as an explicit representation for a neural implicit function. These points are computed and updated on-the-fly during training to capture important geometric features and impose geometric constraints on the optimization. We demonstrate that our method can be adopted to improve state-of-the-art techniques for reconstructing neural implicit surfaces from multi-view images or point clouds. Quantitative and qualitative evaluations show that, compared with existing sampling and optimization methods, our approach allows faster convergence, better generalization, and accurate recovery of details and topology.

关键词： Training Deep learning Surface reconstruction Three-dimensional displays Optimization methods Topology pattern recognition

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Deepfake Catcher: Can a Simple Fusion be Effective and Outperform Complex DNNs?

Deepfake Catcher: Can a Simple Fusion be Effective and Outpe...

引用

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

作者： Akshay Agarwal Nalini Ratha IISER Bhopal India University at Buffalo USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite having completely different configurations, deep learning architectures learn a specific set of features that are common across architectures. For example, the initial few layers learn the low-level edge features from the images. Based on this fact, in this research, we have showcased the potential of deep neural network fusion for simple and effective deepfake detection. The advantage of building an architecture in such a manner is to build a low-power-consuming and accurate defense that can be deployed on mobile devices. To utilize the pre-trained knowledge and obtain downstream task-specific knowledge, we have identified a breakpoint in different networks and divided the obtained knowledge of a network into fixed and adaptive information. We have kept the fixed knowledge intact while modifying the adaptive knowledge along with entirely new knowledge for the deepfake detection task. In the end, the decision of multiple deep architectures trained based on their breakpoint are combined for improved performance. Extensive comparisons performed with existing state-of-the-art architectures demonstrate the effectiveness of the proposed deepfake detection algorithm. The proposed algorithm not only surpasses the existing state-of-the-art (SOTA) algorithms but also needs low computational power. We have further challenged the proposed algorithm by evaluating it by collecting real-world deepfake images.

关键词： Knowledge engineering Image quality Deepfakes Social networking (online) Architecture Deep architecture computer architecture

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Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

Soft-IntroVAE: Analyzing and Improving the Introspective Var...

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

作者： Daniel, Tal Tamar, Aviv Technion Dept Elect Engn Haifa Israel

ISBN: (纸本)9781665445092

The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better understanding of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe an application of Soft-IntroVAE to unsupervised image translation, and demonstrate compelling results. Code and additional information is available on the project website - ***/soft-intro-vae-web.

关键词： Training computer vision Codes Image synthesis Computational modeling Stability analysis Entropy

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Classification of 2D Ultrasound Breast Cancer Images with Deep Learning

Classification of 2D Ultrasound Breast Cancer Images with De...

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

作者： Jack Ellis Kofi Appiah Emmanuel Amankwaa-Frempong Sze Chai Kwok Department of Computer Science University of York York UK Sweden-Ghana Medical Center Accra Ghana Division of Natural & Applied Sciences Duke Kunshan University China Affiliated Mental Health Center School of Psychology & Cognitive Sci. ECNU China Shanghai Changning Mental Health Center Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Breast cancer is the second most prevalent form of cancer and is the “leading cause of most cancer-related deaths in women”. Most women living in low- and middle-income countries (LMIC) have limited access to the existing poor health systems, restricted access to treatment facilities, and in general lack of breast cancer screening programmes. The likelihood of women living in LMIC attending a health facility with advanced-stage breast cancer is very high and the chances of them being able to afford treatment at that stage, even if the treatment is available, is very low. In this work, we evaluate the capabilities of deep learning as a classification tool with the aim of detecting cancerous ultrasound breast images. We aim to deploy a simple classifier on a mobile device with an inexpensive handheld ultrasound imaging system to pick up breast cancer cases that will need medical attention. We demonstrate in this work that with minimal ultrasound images, a de novo system trained from scratch can achieve accuracy of close to 64% and about 78% when the same model is pre-trained.

关键词： Deep learning Ultrasonic imaging Accuracy Computational modeling Transfer learning Breast cancer Mobile handsets

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Pseudo Facial Generation with Extreme Poses for Face recognition

Pseudo Facial Generation with Extreme Poses for Face Recogni...

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

作者： Wang, Guoli Ma, Jiaqi Zhang, Qian Lu, Jiwen Zhou, Jie Tsinghua Univ Beijing Peoples R China Wuhan Univ Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9781665445092

Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances. Our experimental analysis shows a dramatic precision drop with extreme poses. Meanwhile, those extreme poses just exist minor visual differences after small rotations. Derived from this insight, we attempt to relieve such a huge precision drop by making minor changes to the input images without modifying existing discriminators. A novel lightweight pseudo facial generation is proposed to relieve the problem of extreme poses without generating any frontal facial image. It can depict the facial contour information and make appropriate modifications to preserve the critical identity information. Specifically, the proposed method reconstructs pseudo profile faces by minimizing the pixel-wise differences with original profile faces and maintaining the identity consistent information from their corresponding frontal faces simultaneously. The proposed framework can improve existing discriminators and obtain a great promotion on several benchmark datasets.

关键词： Visualization computer vision Image recognition Face recognition Computational modeling Benchmark testing Generators

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