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Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation

Nerfels: Renderable Neural Codes for Improved Camera Pose Es...

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

作者： Avraham, Gil Straub, Julian Shen, Tianwei Yang, Tsun-Yi Germain, Hugo Sweeney, Chris Balntas, Vasileios Novotny, David DeTone, Daniel Newcombe, Richard Monash Univ Clayton Vic Australia Ecole Ponts Champs Sur Marne France Facebook Real Labs Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representation, Nerfels, is locally dense yet globally sparse. As opposed to existing invertible neural rendering systems which overfit a model to the entire scene, we adopt a feature-driven approach for representing scene-agnostic, local 3D patches with renderable codes. By modelling a scene only where local features are detected, our framework effectively generalizes to unseen local regions in the scene via an optimizable code conditioning mechanism in the neural renderer, all while maintaining the low memory footprint of a sparse 3D map representation. Our model can be incorporated to existing state-of-the-art hand-crafted and learned local feature pose estimators, yielding improved performance when evaluating on ScanNet for wide camera baseline scenarios.

关键词： Solid modeling Three-dimensional displays Codes conferences Pose estimation Rendering (computer graphics) Cameras

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MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

MPAF: Model Poisoning Attacks to Federated Learning based on...

引用

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

作者： Cao, Xiaoyu Gong, Neil Zhenqiang Duke Univ Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing model poisoning attacks to federated learning assume that an attacker has access to a large fraction of compromised genuine clients. However, such assumption is not realistic in production federated learning systems that involve millions of clients. In this work, we propose the first Model Poisoning Attack based on Fake clients called MPAF. Specifically, we assume the attacker injects fake clients to a federated learning system and sends carefully crafted fake local model updates to the cloud server during training, such that the learnt global model has low accuracy for many indiscriminate test inputs. Towards this goal, our attack drags the global model towards an attacker-chosen base model that has low accuracy. Specifically, in each round of federated learning, the fake clients craft fake local model updates that point to the base model and scale them up to amplify their impact before sending them to the cloud server. Our experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted, highlighting the need for more advanced defenses.

关键词： Training computer vision Computational modeling conferences Production Collaborative work pattern recognition

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Discriminability-enforcing loss to improve representation learning

Discriminability-enforcing loss to improve representation le...

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

作者： Croitoru, Florinel-Alin Grigore, Diana-Nicoleta Ionescu, Radu Tudor Univ Bucharest Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers. In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers (closer to the output). To this end, we introduce a new loss term inspired by the Gini impurity, which is aimed at minimizing the entropy (increasing the discriminative power) of individual high-level features with respect to the class labels. Although our Gini loss induces highly-discriminative features, it does not ensure that the distribution of the high-level features matches the distribution of the classes. As such, we introduce another loss term to minimize the Kullback-Leibler divergence between the two distributions. We conduct experiments on two image classification data sets (CIFAR-100 and Caltech 101), considering multiple neural architectures ranging from convolutional networks (ResNet-17, ResNet-18, ResNet-50) to transformers (CvT). Our empirical results show that integrating our novel loss terms into the training objective consistently outperforms the models trained with cross-entropy alone, without increasing the inference time at all.

关键词： Training Representation learning Impurities Neural networks computer architecture Transformers Entropy

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Thermal Image Super-Resolution Challenge Results - PBVS 2022

Thermal Image Super-Resolution Challenge Results - PBVS 2022

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

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Kim, Jin Kim, Dogun Li, Zhihao Jian, Yingchun Yan, Bo Cao, Leilei Qi, Fengliang Wang, Hongbin Wu, Rongyuan Sun, Lingchen Zhao, Yongqiang Li, Lin Wang, Kai Wang, Yicheng Zhang, Xuanming Wei, Huiyuan Lv, Chonghua Sun, Qigong Tian, Xiaolin Jia, Zhuang Hu, Jiakui Wang, Chenyang Zhong, Zhiwei Liu, Xianming Jiang, Junjun Escuela Super Politecn Litoral ESPOL Guayaquil Ecuador Comp Vision Ctr Campus UAB Bellaterra 08193 Spain

ISBN: (纸本)9781665487399

This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year's challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community's interest in this hot topic.

关键词： Training Deep learning conferences Superresolution Transformers Particle measurements pattern recognition

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EXCALIBUR: Encouraging and Evaluating Embodied Exploration

EXCALIBUR: Encouraging and Evaluating Embodied Exploration

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2023 ieee/CVF conference on computer vision and pattern recognition, cvpr 2023

作者： Zhu, Hao Kapoor, Raghav Min, So Yeon Han, Winson Li, Jiatai Geng, Kaiwen Neubig, Graham Bisk, Yonatan Kembhavi, Aniruddha Weihs, Luca Carnegie Mellon University United States Allen Institute for Artificial Intelligence

ISBN: (纸本)9798350301298

Experience precedes understanding. Humans constantly explore and learn about their environment out of curiosity, gather information, and update their models of the world. On the other hand, machines are either trained to learn passively from static and fixed datasets, or taught to complete specific goal-conditioned tasks. To encourage the development of exploratory interactive agents, we present the EXCALIBUR benchmark. EXCALIBUR allows agents to explore their environment for long durations and then query their understanding of the physical world via inquiries like: 'is the small heavy red bowl made from glass?' or 'is there a silver spoon heavier than the egg?'. This design encourages agents to perform free-form home exploration without myopia induced by goal conditioning. Once the agents have answered a series of questions, they can renter the scene to refine their knowledge, update their beliefs, and improve their performance on the questions. Our experiments demonstrate the challenges posed by this dataset for the present-day state-of-the-art embodied systems and the headroom afforded to develop new innovative methods. Finally, we present a virtual reality interface that enables humans to seamlessly interact within the simulated world and use it to gather human performance measures. EXCALIBUR affords unique challenges in comparison to presentday benchmarks and represents the next frontier for embodied AI research. © 2023 ieee.

关键词： Embodied vision: Active agents simulation

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Information Elevation Network for Online Action Detection and Anticipation

Information Elevation Network for Online Action Detection an...

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

作者： Min, Sunah Moon, Jinyoung Elect & Telecommun Res Inst ETRI Daejeon South Korea Univ Sci & Technol UST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Given a partially observed video segment, online action detection and anticipation aim to identify a current action and forecast future actions, respectively. To detect actions in a streaming video for monitoring applications including surveillance, robot assistants, and autonomous driving, online action detection methods have been proposed. Considering the importance of current action in online action detection, we introduce a novel information elevation unit (IEU) that lifts and accumulates the past information relevant to the current action, to compensate for forgotten essential information. Using the IEUs, we propose an information elevation network (IEN) that effectively identifies a current action and anticipates future actions through the dense prediction of past and current action classes within the video segment. For its practical use in online monitoring applications, our IEN takes visual features extracted from a fast action recognition using only RGB frames because extracting optical flows requires heavy computation overhead. On THUMOS-14 and TVSeries, our IEN outperforms state-of-the-art methods using only RGB frames. Furthermore, on the THUMOS-14 dataset, our IEN outperforms the state-of-the-art methods.

关键词： Visualization computer vision Surveillance conferences Color Streaming media Logic gates

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VISTA: vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout

VISTA: Vision Transformer enhanced by U-Net and Image Colorf...

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

作者： Shihab, Md Istiak Hossain Tasnim, Nazia Zunair, Hasib Rupty, Labiba Kanij Mohammed, Nabeel Shahjalal Univ Sci & Technol Sylhet Bangladesh Giga Tech Ltd Dhaka Bangladesh Concordia Univ Montreal PQ Canada North South Univ Dhaka Bangladesh

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout. The task is challenging due to the real-world scenario of occlusions where product items overlap, fast movement in conveyor belt, large similarity in overall appearance of the items being scanned, novel products, the negative impact of misidentifying items. Further there is a domain bias between training and test sets, specifically the provided training dataset consists of synthetic images and the test set videos consist of foreign objects such as hands and tray. To address these aforementioned issues, we propose to segment and classify individual frames from a video sequence. The segmentation method consists of a unified single product item- and hand-segmentation followed by entropy masking to address the domain bias problem. The multi-class classification method is based on vision Transformers (ViT). To identify the frames with target objects, we utilize several image processing methods and propose a custom metric to discard frames not having any product items. Combining all these mechanisms, our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with F1 score of 0.4545.

关键词： Measurement Training Image segmentation Urban areas Video sequences Transformers Entropy

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Adversarial Robustness through the Lens of Convolutional Filters

Adversarial Robustness through the Lens of Convolutional Fil...

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

作者： Gavrikov, Paul Keuper, Janis Offenburg Univ IMLA Offenburg Germany Fraunhofer ITWM CC HPC Kaiserslautern Germany Fraunhofer Res Ctr ML Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common defense mechanism is regularization through adversarial training which injects worst-case perturbations back into training to strengthen the decision boundaries, and to reduce overfitting. In this context, we perform an investigation of 3 x 3 convolution filters that form in adversarially-trained models. Filters are extracted from 71 public models of the l(infinity)-RobustBench CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from models built on the same architectures but trained without robust regularization. We observe that adversarially-robust models appear to form more diverse, less sparse, and more orthogonal convolution filters than their normal counterparts. The largest differences between robust and normal models are found in the deepest layers, and the very first convolution layer, which consistently and predominantly forms filters that can partially eliminate perturbations, irrespective of the architecture.

关键词： Training Convolution Perturbation methods computer architecture Predictive models Robustness Data models

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CDAD: A Common Daily Action Dataset with Collected Hard Negative Samples

CDAD: A Common Daily Action Dataset with Collected Hard Nega...

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

作者： Xiang, Wangmeng Li, Chao Li, Ke Wang, Biao Hua, Xian-Sheng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The research on action understanding has achieved significant progress with the establishment of various benchmark datasets. However, the results of action understanding are far from satisfactory in practice. One reason is that the existing action datasets ignore the existence of many hard negative samples in real-world scenarios, which are usually undefined confusion actions, e.g., holding a pen near the mouth vs. smoking. In this work, we focus on the common actions in our daily life and present a novel Common Daily Action Dataset (CDAD), which consists of 57,824 video clips of 23 well-defined common daily actions with rich manual annotations. Particularly, for each daily action, we collect not only diverse positive samples but also various hard negative samples that have minor differences (share similarities) in action with the positive ones. The established CDAD dataset could not only serve as a benchmark for several important daily action understanding tasks, including multi-label action recognition, temporal action localization, and spatial-temporal action detection, but also provide a testbed for researchers to investigate the influence of highly similar negative samples in learning action understanding models.

关键词： Location awareness computer vision Codes conferences Computational modeling Mouth Manuals

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MixAugment & Mixup: Augmentation Methods for Facial Expression recognition

MixAugment & Mixup: Augmentation Methods for Facial Expressi...

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

作者： Psaroudakis, Andreas Kollias, Dimitrios Natl Tech Univ Athens Athens Greece Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Automatic Facial Expression recognition (FER) has attracted increasing attention in the last 20 years since facial expressions play a central role in human communication. Most FER methodologies utilize Deep Neural Networks (DNNs) that are powerful tools when it comes to data analysis. However, despite their power, these networks are prone to overfitting, as they often tend to memorize the training data. What is more, there are not currently a lot of in-the-wild (i.e. in unconstrained environment) large databases for FER. To alleviate this issue, a number of data augmentation techniques have been proposed. Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. One such technique, which has positively contributed to various classification tasks, is Mixup. According to this, a DNN is trained on convex combinations of pairs of examples and their corresponding labels. In this paper, we examine the effectiveness of Mixup for in-the-wild FER in which data have large variations in head poses, illumination conditions, backgrounds and contexts. We then propose a new data augmentation strategy which is based on Mixup, called MixAugment. According to this, the network is trained concurrently on a combination of virtual examples and real examples;all these examples contribute to the overall loss function. We conduct an extensive experimental study that proves the effectiveness of MixAugment over Mixup and various state-of-the-art methods. We further investigate the combination of dropout with Mixup and MixAugment, as well as the combination of other data augmentation techniques with MixAugment.

关键词： Deep learning computer vision Databases Face recognition conferences Neural networks Training data

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