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Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation

Dedicated Inference Engine and Binary-Weight Neural Networks...

引用

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

作者： Tse-Wei Chen Wei Tao Dongyue Zhao Kazuhiro Mima Tadayuki Ito Kinya Osa Masami Kato Device Technology Development Headquarters Canon Inc. Tokyo Japan Canon Innovative Solution (Beijing) Co. Ltd. Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Binary-weight Neural Networks (BNNs), in which weights are binarized and activations are quantized, are employed to reduce computational costs of various kinds of applications. In this paper, a design methodology of hardware architecture for inference engines is proposed to handle modern BNNs with two operation modes. Multiply-Accumulate (MAC) operations can be simplified by replacing multiply operations with bitwise operations. The proposed method can effectively reduce the gate count of inference engines by removing a part of computational costs from the hardware system. The architecture of MAC operations can calculate the inference results of BNNs efficiently with only 52% of hardware costs compared with the related works. To show that the inference engine can handle practical applications, two lightweight networks which combine the backbones of SegNeXt and the decoder of SparseInst for instance segmentation are also proposed. The output results of the lightweight networks are computed using only bitwise operations and add operations. The proposed inference engine has lower hardware costs than related works. The experimental results show that the proposed inference engine can handle the proposed instance-segmentation networks and achieves higher accuracy than YOLACT on the "Person" category although the model size is 77.7× smaller compared with YOLACT.

关键词： Instance segmentation Costs Accuracy Design methodology Neural networks computer architecture Logic gates

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NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

NTIRE 2023 Challenge on 360° Omnidirectional Image and Vide...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Cao, Mingdeng Mou, Chong Yu, Fanghua Wang, Xintao Zheng, Yinqiang Zhang, Jian Dong, Chao Li, Gen Shan, Ying Timofte, Radu Sun, Xiaopeng Li, Weiqi Sheng, Xuhan Chen, Bin Ma, Haoyu Cheng, Ming Zhao, Shijie Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Wu, Renlong Yang, Yi Zhang, Zhilu Zhang, Shuohao Li, Junyi Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Yang, Hao-Hsiang Chen, Yi-Chung Huang, Zhi-Kai Chen, Wei-Ting Chiang, Yuan-Chun Chang, Hua-En Chen, I.-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Zhang, Zebin Zhang, Jiaqi Wang, Yuhui Cui, Shuhao Huang, Junshi Zhu, Li Tian, Shuman Yu, Wei Luo, Bingchun Cui, Wanwan Xu, Tianyu Li, Chunyang Bao, Long Sun, Heng Zhang, Zhenyu Wang, Qian The University of Tokyo Japan Arc Lab Tencent Pcg China Peking University China Shenzhen Institute of Advanced Technology Cas China Platform Technologies Tencent Online Video China Computer Vision Lab Ifi & Caidas University of Würzburg Germany ByteDance China Peking University Shenzhen Graduate School China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China Harbin Institute of Technology China 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 ServiceNow United States ShanghaiTech University China Meituan China Xiaomi Inc

ISBN: (纸本)9798350302493

This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets. © 2023 ieee.

关键词： Image compression

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Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches

Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised...

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

作者： Yi Li Yunan Wu Aggelos K. Katsaggelos The Department of Computer Science Northwestern University Evanston IL The Department of Electrical Computer Engineering Northwestern University Evanston IL

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The advancement of The Laser Interferometer Gravitational-Wave Observatory (LIGO) has significantly enhanced the feasibility and reliability of gravitational wave detection. However, LIGO’s high sensitivity makes it susceptible to transient noises known as glitches, which necessitate effective differentiation from real gravitational wave signals. Traditional approaches predominantly employ fully supervised or semi-supervised algorithms for the task of glitch classification and clustering. In the future task of identifying and classifying glitches across main and auxiliary channels, it is impractical to build a dataset with manually labeled ground-truth. In addition, the patterns of glitches can vary with time, generating new glitches without manual labels. In response to this challenge, we introduce the Cross-Temporal Spectrogram Autoencoder (CTSAE), a pioneering unsupervised method for the dimensionality reduction and clustering of gravitational wave glitches. CTSAE integrates a novel four-branch autoencoder with a hybrid of Convolutional Neural Networks (CNN) and vision Transformers (ViT). To further extract features across multi-branches, we introduce a novel multi-branch fusion method using the CLS (Class) token. Our model, trained and evaluated on the GravitySpy O3 dataset on the main channel, demonstrates superior performance in clustering tasks when compared to state-of-the-art semi-supervised learning methods. To the best of our knowledge, CTSAE represents the first unsupervised approach tailored specifically for clustering LIGO data, marking a significant step forward in the field of gravitational wave research. The code of this paper is available at https://***/Zod-L/CTSAE

关键词： Dimensionality reduction computer vision Sensitivity Gravitational waves Sensor phenomena and characterization Feature extraction Transformers

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NTIRE 2021 Learning the Super-Resolution Space Challenge

NTIRE 2021 Learning the Super-Resolution Space Challenge

引用

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

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Busch, Christoph Chen, Yang Cheng, Jian Chudasama, Vishal Gang, Ruipeng Gao, Shangqi Gao, Kun Gong, Laiyun Han, Qingrui Huang, Chao Jin, Zhi Jo, Younghyun Kim, Seon Joo Kim, Younggeun Lee, Seungjun Lei, Yuchen Li, Chu-Tak Li, Chenghua Li, Ke Liu, Zhi-Song Liu, Youming Nan, Nan Park, Seung-Ho Patel, Heena Peng, Shichong Prajapati, Kalpesh Qi, Haoran Raja, Kiran Ramachandra, Raghavendra Siu, Wan-Chi Son, Donghee Song, Ruixia Upla, Kishor Wang, Li-Wen Wang, Yatian Wang, Junwei Wu, Qianyu Xu, Xinhua Yang, Sejong Yuan, Zhen Zhang, Liting Zhang, Huanrong Zhang, Junkai Zhang, Yifan Zhang, Zhenzhou Zhou, Hangqi Zhu, Aichun Zhuang, Xiahai Zou, Jiaxin Swiss Fed Inst Technol Zurich Switzerland Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Zhejiang Univ Hangzhou Peoples R China NetEaseYunXin Beijing Peoples R China Yonsei Univ Seoul South Korea Seoul Natl Univ Seoul South Korea Univ Ulsan Coll Med Asan Med Ctr Ulsan South Korea Lomin Inc Portland OR USA Fudan Univ Shanghai Peoples R China Simon Fraser Univ Burnaby BC Canada Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China North China Univ Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China CASIA Beijing Peoples R China NRTA Beijing Peoples R China NCUT Beijing Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Southeast Univ Lab Image Sci & Technol Nanjing Peoples R China SVNIT Surat Gujarat India NTNU Trondheim Norway

ISBN: (纸本)9781665448994

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a single SR image. The goal of the challenge is to spur research into developing learning formulations and models better suited for the highly ill-posed SR problem. And thereby advance the state-of-the-art in the broader SR field. In order to evaluate the quality of the predicted SR space, we propose a new evaluation metric and perform a comprehensive analysis of the participating methods. The challenge contains two tracks: 4x and 8 x scale factor. In total, 11 teams competed in the final testing phase.

关键词： Measurement computer vision conferences Superresolution Predictive models pattern recognition

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DMR: Disentangling Marginal Representations for Out-of-Distribution Detection

DMR: Disentangling Marginal Representations for Out-of-Distr...

引用

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

作者： Dasol Choi Dongbin Na MODULABS Yonsei University Pohang University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Out-of-Distribution (OOD) detection is crucial for the reliable deployment of deep-learning applications. When a given input image does not belong to any categories of the deployed classification model, the classification model is expected to alert the user that the predicted outputs might be unreliable. Recent studies have shown that utilizing a large amount of explicit OOD training data is helpful for improving OOD detection performance. However, collecting explicit real-world OOD data is burdensome, and pre-defining all out-of-distribution labels is fundamentally difficult. In this work, we present a novel method, Disentangling Marginal Representations (DMR), that generates artificial OOD training data by extracting marginal features from images of an In-Distribution (ID) training dataset and manipulating these extracted marginal representations. DMR is intuitive and can be used as a realistic solution that does not require any extra real-world OOD data. Moreover, our method can be simply applied to pre-trained classifier networks without affecting the original classification performance. We demonstrate that a shallow rejection network that is trained on the small subset of synthesized OOD training data generated from our method and attachable to the classifier network achieves superior OOD detection performance. With extensive experiments, we show that our proposed method significantly outperforms the state-of-the-art OOD detection methods on the broadly used CIFAR-10 and CIFAR-100 detection benchmark datasets. We also demonstrate that our proposed method can be further improved when combined with existing methods. The source codes are publicly available at https://***/ndb796/DMR.

关键词： Training computer vision Source coding conferences Training data Predictive models Benchmark testing

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Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speech Affective Face and Body Expressions from Affordable Inputs

Speech2UnifiedExpressions: Synchronous Synthesis of Co-Speec...

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

作者： Uttaran Bhattacharya Aniket Bera Dinesh Manocha Adobe Inc. San Jose CA USA Purdue University West Lafayette IN USA University of Maryland College Park MD USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a multimodal learning-based method to simultaneously synthesize co-speech facial expressions and upper-body gestures for digital characters using RGB video data captured using commodity cameras. Our approach learns from sparse face landmarks and upper-body joints, estimated directly from video data, to generate plausible emotive character motions. Given a speech audio waveform and a token sequence of the speaker’s face landmark motion and body-joint motion computed from a video, our method synthesizes the motion sequences for the speaker’s face landmarks and body joints to match the content and the affect of the speech. We design a generator consisting of a set of encoders to transform all the inputs into a multimodal embedding space capturing their correlations, followed by a pair of decoders to synthesize the desired face and pose motions. To enhance the plausibility of synthesis, we use an adversarial discriminator that learns to differentiate between the face and pose motions computed from the original videos and our synthesized motions based on their affective expressions. To evaluate our approach, we extend the TED Gesture Dataset to include view-normalized, co-speech face landmarks in addition to body gestures. We demonstrate the performance of our method through thorough quantitative and qualitative experiments on multiple evaluation metrics and via a user study. We observe that our method results in low reconstruction error and produces synthesized samples with diverse facial expressions and body gestures for digital characters. The relevant source code and dataset are available at https://***/UttaranB127/speech2unified_expressions.

关键词： Measurement Learning systems computer vision Correlation Face recognition Source coding conferences

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Towards Efficient Machine Unlearning with Data Augmentation: Guided Loss-Increasing (GLI) to Prevent the Catastrophic Model Utility Drop

Towards Efficient Machine Unlearning with Data Augmentation:...

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

作者： Dasol Choi Soora Choi Eunsun Lee Jinwoo Seo Dongbin Na Yonsei University MODULABS Chung Ang University Kyung Hee University Millennialsworks Inc. POSTECH

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Machine unlearning algorithms aim to make a model forget specific data that might be used in the training phase. To solve this problem, various studies have adopted loss-increasing methods. For example, some unlearning methods have presented data augmentation methods to generate synthesized images that maximize loss values for images to be forgotten. In contrast, some unlearning methods directly update the model in the direction of increasing loss for the images to be forgotten. In this paper, we first revisit these loss-increasing methods and analyze their limitations. We have found that these simple loss-increasing strategies can be effective in the aspect of the forgetting score, however, can hurt the original model utility unexpectedly, we call this phenomenon catastrophic model utility drop. We propose a novel data augmentation method, Guided Loss-Increasing (GLI), that restricts the direction of the data update to resolve the utility drop issue. This is achieved by aligning updates with the model’s existing knowledge, thereby ensuring that the unlearning process does not adversely affect the model’s original performance. Our extensive experiments demonstrate our method shows superior (1) model utility and (2) forgetting performance compared to the previous state-of-the-art (SOTA) methods. Furthermore, we demonstrate Jensen–Shannon divergence can be utilized to robustly evaluate the forgetting score. The source codes are publicly available at https://***/Dasol-Choi/Guided_Loss_Increasing.

关键词： Measurement Training computer vision Source coding conferences Computational modeling Data augmentation

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Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach

Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly G...

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

作者： Ayush K. Rai Tarun Krishna Feiyan Hu Alexandru Drimbarean Kevin McGuinness Alan F. Smeaton Noel E. O’Connor Insight SFI Centre for Data Analytics Dublin City University Tobii Corporation Galway

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances. Recent works have investigated the creation of pseudo-anomalies (PAs) using only the normal data and making strong assumptions about real-world anomalies with regards to abnormality of objects and speed of motion to inject prior information about anomalies in an autoencoder (AE) based reconstruction model during training. This work proposes a novel method for generating generic spatio-temporal PAs by inpainting a masked out region of an image using a pre-trained Latent Diffusion Model and further perturbing the optical flow using mixup to emulate spatio-temporal distortions in the data. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting by learning three types of anomaly indicators, namely reconstruction quality, temporal irregularity and semantic inconsistency. Extensive experiments on four VAD benchmark datasets namely Ped2, Avenue, ShanghaiTech and UBnormal demonstrate the effectiveness of our work against other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting. Our analysis also examines the transferability and generalisation of PAs across these datasets, offering valuable insights by identifying real-world anomalies through PAs. Our results can be reproduced on github.

关键词： Training Semantics Training data Optical distortion Distortion pattern recognition Image reconstruction

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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

作者： Munkhjargal Gochoo Munkh-Erdene Otgonbold Erkhembayar Ganbold Jun-Wei Hsieh Ming-Ching Chang Ping-Yang Chen Byambaa Dorj Hamad Al Jassmi Ganzorig Batnasan Fady Alnajjar Mohammed Abduljabbar Fang-Pang Lin Department of Computer Science and Software Engineering United Arab Emirates University UAE Emirates Center for Mobility Research United Arab Emirates University UAE College of AI and Green Energy National Yang Ming Chiao Tung University Taiwan University at Albany — State University of New York NY USA Department of Computer Science National Yang Ming Chiao Tung University Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.

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Analyzing Participants’ Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features

Analyzing Participants’ Engagement during Online Meetings U...

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

作者： Alexander Vedernikov Zhaodong Sun Virpi-Liisa Kykyri Mikko Pohjola Miriam Nokia Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Psychology University of Jyväskylä Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement measurement finds application in healthcare, education, services. The use of physiological and behavioral features is viable, but the impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of unsupervised remote photoplethysmography (rPPG) as an alternative for contact sensors in deriving heart rate variability (HRV) features, then fusing these with behavioral features to measure engagement in online group meetings. Firstly, a unique Engagement Dataset of online interactions among social workers is collected with granular engagement labels, offering insight into virtual meeting dynamics. Secondly, a pre-trained rPPG model is customized to reconstruct rPPG signals from video meetings in an unsupervised manner, enabling the calculation of HRV features. Thirdly, the feasibility of estimating engagement from HRV features using short observation windows, with a notable enhancement when using longer observation windows of two to four minutes, is demonstrated. Fourthly, the effectiveness of behavioral cues is evaluated when fused with physiological data, which further enhances engagement estimation performance. An accuracy of 94% is achieved when only HRV features are used, eliminating the need for contact sensors or ground truth signals; use of behavioral cues raises the accuracy to 96%. Facial analysis offers precise engagement measurement, beneficial for future applications.

关键词： Accuracy Atmospheric measurements Estimation Web conferencing Sensor phenomena and characterization Photoplethysmography Particle measurements

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