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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

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

作者： Alexander Wong Yifan Wu Saad Abbasi Saeejith Nair Yuhao Chen Mohammad Javad Shafiee Vision and Image Processing Research Group University of Waterloo Waterloo Artificial Intelligence Institute Waterloo ON DarwinAI Corp. Waterloo ON

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor.

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NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results

NTIRE 2023 Challenge on Image Super-Resolution (×4): Method...

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

作者： Zhang, Yulun Zhang, Kai Chen, Zheng Li, Yawei Timofte, Radu Zhang, Junpei Zhang, Kexin Peng, Rui Ma, Yanbiao Jiao, Licheng Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Qiu, Yajun Zhu, Qiang Li, Pengfei Li, Qianhui Zhu, Shuyuan Zhang, Dafeng Li, Jia Wang, Fan Li, Chunmiao Kim, TaeHyung Kil, Jungkeong Kim, Eon Yu, Yeonseung Lee, Beomyeol Lee, Subin Lim, Seokjae Chae, Somi Choi, Heungjun Huang, ZhiKai Chen, YiChung Chiang, YuanChun Yang, HaoHsiang Chen, WeiTing Chang, HuaEn Chen, I-Hsiang Hsieh, ChiaHsuan Kuo, SyYen Choi, Ui-Jin Conde, Marcos V. Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Wei, Jiayu Li, Yanfeng Sun, Jia Cheng, Zhanyi Li, Zhiyuan Yao, Xu Wang, Xinyi Li, Danxu Cui, Xuan Cao, Jun Li, Cheng Zheng, Jianbin Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Computer Vision Lab Eth Zurich Switzerland Shanghai Jiao Tong University China University of Würzburg Germany Xidian University 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 School of Information and Communication Engineering University of Electronic Science and Technology of China China China Lotte Data Communication Company Seoul Korea Republic of 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 MegaStudyEdu Korea Republic of Computer Vision Lab Caidas University of Würzburg Germany University of Sindh Pakistan Iklab Inc. Tech University of Korea Siheung-Si Korea Republic of Micro-Nano Electronics Department Shanghai Jiao Tong University China Electrical Engineering Department Faculty of Engineering Assiut University Egypt Hefei University of Technology China Beijing Jiaotong University China South China University of Technology Guangdong Guangzhou China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image super-resolution (×4), focusing on the proposed solutions and results. The task of image super-resolution (SR) is to generate a high-resolution (HR) output from a corresponding low-resolution (LR) input by leveraging prior information from paired LR-HR images. The aim of the challenge is to obtain a network design/solution capable to produce high-quality results with the best performance (e.g., PSNR). We want to explore how high performance we can achieve regardless of computational cost (e.g., model size and FLOPs) and data. The track of the challenge was to measure the restored HR images with the ground truth HR images on DIV2K testing dataset. The ranking of the teams is determined directly by the PSNR value. The challenge has attracted 192 registered participants, where 15 teams made valid submissions. They achieve state-of-the-art performance in single image super-resolution. © 2023 ieee.

关键词： Statistical tests

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MultiPanoWise: holistic deep architecture for multi-task dense prediction from a single panoramic image

MultiPanoWise: holistic deep architecture for multi-task den...

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

作者： Uzair Shah Muhammad Tukur Mahmood Alzubaidi Giovanni Pintore Enrico Gobbetti Mowafa Househ Jens Schneider Marco Agus ICT Division College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Visual and Data-Intensive Computing CRS4 Italy National Research Center in HPC Big Data and Quantum Computing Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a novel holistic deep-learning approach for multi-task learning from a single indoor panoramic image. Our framework, named MultiPanoWise, extends vision transformers to jointly infer multiple pixel-wise signals, such as depth, normals, and semantic segmentation, as well as signals from intrinsic decomposition, such as reflectance and shading. Our solution leverages a specific architecture combining a transformer-based encoder-decoder with multiple heads, by introducing, in particular, a novel context adjustment approach, to enforce knowledge distillation between the various signals. Moreover, at training time we introduce a hybrid loss scalarization method based on an augmented Chebychev/hypervolume scheme. We illustrate the capabilities of the proposed architecture on public-domain synthetic and real-world datasets. We demonstrate performance improvements with respect to the most recent methods specifically designed for single tasks, like, for example, individual depth estimation or semantic segmentation. To our knowledge, this is the first architecture capable of achieving state-of-the-art performance on the joint extraction of heterogeneous signals from single indoor omnidirectional images.

关键词： Training Reflectivity computer vision Semantic segmentation Estimation computer architecture Multitasking

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Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues

Joint Physical-Digital Facial Attack Detection Via Simulatin...

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

作者： Xianhua He Dashuang Liang Song Yang Zhanlong Hao Hui Ma Binjie Mao Xi Li Yao Wang Pengfei Yan Ajian Liu Vision AI Department Meituan M.U.S.T MAIS CASIA China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are frequently subjected to a variety of physical and digital attacks of different types. Previous methods have achieved satisfactory performance in scenarios that address physical attacks and digital attacks, respectively. However, few methods are considered to integrate a model that simultaneously addresses both physical and digital attacks, implying the necessity to develop and maintain multiple models. To jointly detect physical and digital attacks within a single model, we propose an innovative approach that can adapt to any network architecture. Our approach mainly contains two types of data augmentation, which we call Simulated Physical Spoofing Clues augmentation (SPSC) and Simulated Digital Spoofing Clues augmentation (SDSC). SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types. Extensive experiments show that SPSC and SDSC can achieve state-of-the-art generalization in Protocols 2.1 and 2.2 of the UniAttackData dataset, respectively. Our method won first place in "Unified Physical-Digital Face Attack Detection" of the 5th Face Anti-spoofing Challenge@CVPR2024. Our final submission obtains 3.75% APCER, 0.93% BPCER, and 2.34% ACER, respectively. Our code is available at https://***/Xianhua-He/cvpr2024-face-anti-spoofing-challenge.

关键词： Adaptation models computer vision Protocols Codes Face recognition conferences Computational modeling

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NORPPA: NOvel Ringed Seal Re-Identification by Pelage pattern Aggregation

NORPPA: NOvel Ringed Seal Re-Identification by Pelage Patter...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Ekaterina Nepovinnykh Tuomas Eerola Heikki Kälviäinen Ilia Chelak Department of Computational Engineering School of Engineering Sciences Computer Vision and Pattern Recognition Laboratory (CVPRL) Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Computer Science Faculty of Science University of Helsinki Helsinki Finland

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

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Swift Parameter-free Attention Network for Efficient Super-Resolution

Swift Parameter-free Attention Network for Efficient Super-R...

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

作者： Cheng Wan Hongyuan Yu Zhiqi Li Yihang Chen Yajun Zou Yuqing Liu Xuanwu Yin Kunlong Zuo Georgia Institute of Technology Xiaomi Inc

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Single Image Super-Resolution (SISR) is a crucial task in low-level computer vision, aiming to reconstruct high-resolution images from low-resolution counterparts. Conventional attention mechanisms have significantly improved SISR performance but often result in complex network structures and large number of parameters, leading to slow inference speed and large model size. To address this issue, we propose the Swift Parameter-free Attention Network (SPAN), a highly efficient SISR model that balances parameter count, inference speed, and image quality. SPAN employs a novel parameter-free attention mechanism, which leverages symmetric activation functions and residual connections to enhance high-contribution information and suppress redundant information. Our theoretical analysis demonstrates the effectiveness of this design in achieving the attention mechanism’s purpose. We evaluate SPAN on multiple benchmarks, showing that it outperforms existing efficient super-resolution models in terms of both image quality and inference speed, achieving a significant quality-speed trade-off. This makes SPAN highly suitable for real-world applications, particularly in resource-constrained scenarios. Notably, we won the first place both in the overall performance track and runtime track of the NTIRE 2024 efficient super-resolution challenge. Our code and models are made publicly available at https://***/hongyuanyu/span.

关键词： Image quality Performance evaluation computer vision Attention mechanisms Runtime Computational modeling Superresolution

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QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

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

作者： Piotr Kluska Adrián Castelló Florian Scheidegger A. Cristiano I. Malossi Enrique S. Quintana-Ortí IBM Research Europe Universitat Politècnica de València Universitat Politècnica de València IBM Research Europe

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers –linear layer and attention– on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 ± 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： computer vision Image segmentation Quantization (signal) Computational modeling Graphics processing units Prototypes Transformers

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Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT

Mitigating Challenges of the Space Environment for Onboard A...

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

作者： Miguel Ortiz Del Castillo Jonathan Morgan Jack McRobbie Clint Therakam Zaher Joukhadar Robert Mearns Simon Barraclough Richard Sinnott Andrew Woods Chris Bayliss Kris Ehinger Ben Rubinstein James Bailey Airlie Chapman Michele Trenti The University of Melbourne

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control. Here, we present the hardware and software design of an onboard AI subsystem hosted on SpIRIT. The system is optimised for on-board computer vision experiments based on visible light and long wave infrared cameras. This paper highlights the key design choices made to maximise the robustness of the system in harsh space conditions, and their motivation relative to key mission requirements, such as limited compute resources, resilience to cosmic radiation, extreme temperature variations, distribution shifts, and very low transmission bandwidths. The payload, called Loris, consists of six visible light cameras, three infrared cameras, a camera control board and a Graphics Processing Unit (GPU) system-on-module. Loris enables the execution of AI models with on-orbit fine-tuning as well as a next-generation image compression algorithm, including progressive coding. This innovative approach not only enhances the data processing capabilities of nanosatellites but also lays the groundwork for broader applications to remote sensing from space.

关键词： computer vision Temperature distribution Image coding Space missions Small satellites Graphics processing units Aerospace electronics

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Multi-camera People Tracking With Mixture of Realistic and Synthetic Knowledge

Multi-camera People Tracking With Mixture of Realistic and S...

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

作者： Quang Qui-Vinh Nguyen Huy Dinh-Anh Le Truc Thi-Thanh Chau Duc Trung Luu Nhat Minh Chung Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

This paper presents a solution for Track 1 of the AI City Challenge 2023, which involves Multi-Camera People Tracking in indoor scenarios. The proposed framework comprises four modules: Vehicle detection, ReID feature extraction, single-camera multi-target tracking (SCMT), single-camera matching, and multi-camera matching. A significant contribution of our approach is the introduction of ID switch detection and ID switch splitting using the Gaussian mixture model, which efficiently addresses the problem of tracklets with ID switches. Furthermore, our system performs well in matching both synthetic and real data. The proposed R-matching algorithm performs exceptionally well in real scenarios despite being trained on synthetic data. Experimental results on the public test set of 2023 AI City Challenge Track 1 demonstrate the efficacy of the proposed approach, achieving an IDF1 of 94.17% and securing 2nd position on the leaderboard. Codes will be available at https://***/nguyenquivinhquang/Multi-camera-People-Tracking-With-Mixture-of-Realistic-and-Synthetic-Knowledge

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3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data

3D Kinematics Estimation from Video with a Biomechanical Mod...

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

作者： Zhi-Yi Lin Bofan Lyu Judith Cueto Fernandez Eline van der Kruk Ajay Seth Xucong Zhang Department of Intelligent Systems Computer Vision Lab Delft University of Technology Department of BioMechanical Engineering Faculty of Mechanical Engineering Delft University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement. Conventional marker-based motion capture is expensive in terms of financial investment, time, and the expertise required. Moreover, due to the scarcity of datasets with accurate annotations, existing markerless motion capture methods suffer from challenges including unreliable 2D keypoint detection, limited anatomic accuracy, and low generalization capability. In this work, we propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views with consideration of biomechanical prior and spatio-temporal information. To train the model, we create synthetic dataset ODAH with accurate kinematics annotations generated by aligning the body mesh from the SMPL-X model and a full-body OpenSim skeletal model. Our extensive experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods when evaluated across multiple datasets, revealing a promising direction for enhancing video-based human motion capture.

关键词： Biomechanics Visualization computer vision Accuracy Three-dimensional displays Biological system modeling Estimation

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