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Endpoints Weight Fusion for Class Incremental Semantic Segmentation

Endpoints Weight Fusion for Class Incremental Semantic Segme...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jia–Wen Xiao Chang–Bin Zhang Jiekang Feng Xialei Liu Joost van de Weijer Ming–Ming Cheng VCIP CS Nankai University Tianjin University Computer Vision Center Universitat Autònoma de Barcelona

Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploits regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model since only the representations of old and new models are restricted to be consistent. In this paper, we propose a simple yet effective method to obtain a model with a strong memory of old knowledge, named Endpoints Weight Fusion (EWF). In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever changing distributions. In addition, we analyze the relationship between our fusion strategy and a popular moving average technique EMA, which reveals why our method is more suitable for class-incremental learning. To facilitate parameter fusion with closer distance in the parameter space, we use distillation to enhance the optimization process. Furthermore, we conduct experiments on two widely used datasets, achieving state-of-the-art performance.

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TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action recognition

TimeBalance: Temporally-Invariant and Temporally-Distinctive...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ishan Rajendrakumar Dave Mamshad Nayeem Rizve Chen Chen Mubarak Shah Center for Research in Computer Vision University of Central Florida Orlando USA

Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher an-notation cost and dimensionality. Besides, any video understanding task requires reasoning over both spatial and temporal dimensions. In order to learn both the static and motion related features for the semi-supervised action recognition task, existing methods rely on hard in-put inductive biases like using two-modalities (RGB and Optical-flow) or two-stream of different playback rates. Instead of utilizing unlabeled videos through diverse in-put streams, we rely on self-supervised video represen-tations, particularly, we utilize temporally-invariant and temporally-distinctive representations. We observe that these representations complement each other depending on the nature of the action. Based on this observation, we propose a student-teacher semi-supervised learning frame-work, TimeBalance, where we distill the knowledge from a temporally-invariant and a temporally-distinctive teacher. Depending on the nature of the unlabeled video, we dy-namically combine the knowledge of these two teach-ers based on a novel temporal similarity-based reweighting scheme. Our method achieves state-of-the-art performance on three action recognition benchmarks: UCF101, HMDB51, and Kinetics400. Code: https://***/DAVEISHAN/TimeBalance.

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The Translucent Patch: A Physical and Universal Attack on Object Detectors

The Translucent Patch: A Physical and Universal Attack on Ob...

引用

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

作者： Zolfi, Alon Kravchik, Moshe Elovici, Yuval Shabtai, Asaf Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel

ISBN: (纸本)9781665445092

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical patch containing a carefully constructed pattern, which is placed on the camera's lens, to fool state-of-the-art object detectors. The primary goal of our patch is to hide all instances of a selected target class. In addition, the optimization method used to construct the patch aims to ensure that the detection of other (untargeted) classes remains unharmed. Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes. We show that our patch was able to prevent the detection of 42.27% of all stop sign instances while maintaining high (nearly 80%) detection of the other classes.

关键词： computer vision Face recognition Optimization methods Detectors Object detection Cameras Autonomous vehicles

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POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery

POTTER: Pooling Attention Transformer for Efficient Human Me...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ce Zheng Xianpeng Liu Guo-Jun Qi Chen Chen Center for Research in Computer Vision University of Central Florida North Carolina State University OPPO Seattle Research Center USA Westlake University

Transformer architectures have achieved SOTA performance on the human mesh recovery (HMR) from monocular images. However, the performance gain has come at the cost of substantial memory and computational overhead. A lightweight and efficient model to reconstruct accurate human mesh is needed for real-world applications. In this paper, we propose a pure transformer architecture named POoling aTtention TransformER (POTTER) for the HMR task from single images. Observing that the conventional attention module is memory and computationally expensive, we propose an efficient pooling attention module, which significantly reduces the memory and computational cost without sacrificing performance. Furthermore, we design a new transformer architecture by integrating a High-Resolution (HR) stream for the HMR task. The high-resolution local and global features from the HR stream can be utilized for recovering more accurate human mesh. Our POTTER outperforms the SOTA method METRO by only requiring 7% of total parameters and 14% of the Multiply-Accumulate Operations on the Human3.6M (PA-MPJPE metric) and 3DPW (all three metrics) datasets. The project webpage is https://***/potter_page/.

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引用

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

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781665445092

Deep convolutional neural networks (CNNs) leverage large-scale training dataset to produce remarkable performance on various image classification tasks. It, however, is difficult to effectively train the CNNs on some realistic learning situations such as regarding class imbalance, small-scale and label noises. Regularizing CNNs works well on learning with such deteriorated training datasets by mitigating overfitting issues. In this work we propose a method to effectively impose regularization on feature representation learning. By focusing on the angle between a feature and a classifier which is embedded in cosine similarity at the classification layer, we formulate a novel similarity beyond the cosine based on von Mises-Fisher distribution of directional statistics. In contrast to the cosine similarity, our similarity is compact while having heavy tail, which contributes to regularizing intra-class feature distribution to improve generalization performance. Through the experiments on some realistic learning situations such as of unbalance, small-scale and noisy labels, we demonstrate the effectiveness of the proposed method for training CNNs, in comparison to the other regularization methods. Codes are available at https://***/tk1980/tvMF.

关键词： Training computer vision Computational modeling Focusing pattern recognition Noise measurement Convolutional neural networks

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Complementary Intrinsics from Neural Radiance Fields and CNNs for Outdoor Scene Relighting

Complementary Intrinsics from Neural Radiance Fields and CNN...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Siqi Yang Xuanning Cui Yongjie Zhu Jiajun Tang Si Li Zhaofei Yu Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University Institute for Artificial Intelligence Peking University AI Innovation Center School of Computer Science Peking University School of Artificial Intelligence Beijing University of Posts and Telecommunications

Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multiview stereo. With neural radiance fields (NeRF), editing the appearance code could produce more realistic results without interpreting the outdoor scene image formation explicitly. This paper proposes to complement the intrinsic estimation from volume rendering using NeRF and from inversing the photometric image formation model using convolutional neural networks (CNNs). The former produces richer and more reliable pseudo labels (cast shadows and sky appearances in addition to albedo and normal) for training the latter to predict interpretable and editable lighting parameters via a single-image prediction pipeline. We demonstrate the advantages of our method for both intrinsic image decomposition and relighting for various real outdoor scenes.

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View-Guided Point Cloud Completion

View-Guided Point Cloud Completion

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

作者： Zhang, Xuancheng Feng, Yutong Li, Siqi Zou, Changqing Wan, Hai Zhao, Xibin Guo, Yandong Gao, Yue Tsinghua Univ Sch Software KLISS BNRist Beijing Peoples R China Tsinghua Univ THUICBS Beijing Peoples R China Huawei Technol Canada Co Ltd Markham ON Canada OPPO Res Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image. By leveraging a framework that sequentially performs effective cross-modality and cross-level fusions, our method achieves significantly superior results over typical existing solutions on a new large-scale dataset we collect for the view-guided point cloud completion task.

关键词： computer vision Shape Sensor fusion pattern recognition Task analysis Image reconstruction Periodic structures

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NTIRE 2023 Challenge on Night Photography Rendering

NTIRE 2023 Challenge on Night Photography Rendering

引用

2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Shutova, Alina Ershov, Egor Perevozchikov, Georgy Ermakov, Ivan Banić, Nikola Timofte, Radu Collins, Richard Efimova, Maria Terekhin, Arseniy Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Lei, Chunxia Wang, Tingniao Wang, Song Liu, Shuai Feng, Chaoyu Shao, Guangqi Wang, Hao Wang, Xiaotao Lei, Lei Xu, Lu Zhang, Chao Wang, Yasi Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Kinli, Furkan Özcan, Bariş Kiraç, Furkan Chung, Hyerin Lee, Nakyung Kwak, Sung Keun Conde, Marcos Elezabi, Omar Seizinger, Tim Vasluianu, Florin Hsieh, Chia-Hsuan Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Chen, I-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Kuo, Sy-Yen Russia Gideon Brothers Croatia Swiss Federal Institute of Technology in Zurich Switzerland University of Milano - Bicocca Italy Zhejiang Dahua Technology China Xiaomi Inc. China Beijing Samsung Teleconmmunication R&D Center China Vision and Graphics Lab Özyeǧin University Turkey Fishency Innovation Norway T-Fashion Inc. Turkey CJ OliveNetworks AI Research Korea Republic of University of Wuerzburg Computer Vision Lab CAIDAS Germany ServiceNow United States Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan University of Würzburg Germany

ISBN: (纸本)9798350302493

This paper presents a review of the NTIRE 2023 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, participants were not provided with a large training dataset for the target sensor. Instead, this time they were given images of a color checker illuminated by a known light source. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. The highest ranking solutions were further ranked by Richard Collins, a renowned photographer. The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://***/ © 2023 ieee.

关键词： Photography

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DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images

DARCNN: Domain Adaptive Region-based Convolutional Neural Ne...

引用

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

作者： Hsu, Joy Chiu, Wah Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

In the biomedical domain, there is an abundance of dense, complex data where objects of interest may be challenging to detect or constrained by limits of human knowledge. Labelled domain specific datasets for supervised tasks are often expensive to obtain, and furthermore discovery of novel distinct objects may be desirable for unbiased scientific discovery. Therefore, we propose leveraging the wealth of annotations in benchmark computer vision datasets to conduct unsupervised instance segmentation for diverse biomedical datasets. The key obstacle is thus overcoming the large domain shift from common to biomedical images. We propose a Domain Adaptive Region-based Convolutional Neural Network (DARCNN), that adapts knowledge of object definition from COCO, a large labelled vision dataset, to multiple biomedical datasets. We introduce a domain separation module, a self-supervised representation consistency loss, and an augmented pseudo-labelling stage within DARCNN to effectively perform domain adaptation across such large domain shifts. We showcase DARCNN's performance for unsupervised instance segmentation on numerous biomedical datasets.

关键词： Knowledge engineering Image segmentation computer vision Adaptive systems Annotations Benchmark testing pattern recognition

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Deep Learning-based Human Height Estimation from a Stereo vision System 13

Deep Learning-based Human Height Estimation from a Stereo Vi...

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ieee 13th International conference on pattern recognition Systems (ICPRS)

作者： Velesaca, Henry O. Vulgarin, Jorge Vintimilla, Boris X. Escuela Super Polit ecn Litoral ESPOL CIDIS Fac Ingn Elect & Comp Campus Gustavo Galindo Guayaquil 09015863 Ecuador

ISBN: (纸本)9798350333374

This paper presents a deep learning-based human height estimation approach using a stereo vision system, which is part of a smart receptionist framework. The proposal consists of a smart screen with an integrated webcam and an additional webcam. The workflow of this approach initially acquires and processes the images in real-time, using deep neural networks detects a human in the images, aligns the images from the two cameras, then obtains the depth of the camera, and finally computes the height of the person under study. The proposed solution is evaluated by different people to check the effectiveness of the system. Obtained results show MAE below 1.0 cm in the computed heights, it is also compared with other techniques of the state-of-the-art.

关键词： human height estimation stereo vision system deep learning computer vision approaches smart receptionist

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