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Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models

Exploring Real World Map Change Generalization of Prior-Info...

引用

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

作者： Samuel M. Bateman Ning Xu H. Charles Zhao Yael Ben Shalom Vince Gong Greg Long Will Maddern Nuro Inc.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has sparked renewed interest in the online mapping problem. However, effectively predicting online maps at a high enough quality to enable safe, driverless deployments remains a significant challenge. Recent work on these models proposes training robust online mapping systems using low quality map priors with synthetic perturbations in an attempt to simulate out-of-date HD map priors. In this paper, we investigate how models trained on these synthetically perturbed map priors generalize to performance on deployment-scale, real world map changes. We present a large-scale experimental study to determine which synthetic perturbations are most useful in generalizing to real world HD map changes, evaluated using multiple years of real-world autonomous driving data. We show there is still a substantial sim2real gap between synthetic prior perturbations and observed real-world changes, which limits the utility of current prior-informed HD map prediction models.

关键词： Training computer vision Perturbation methods conferences Noise Buildings Predictive models

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Tackling Domain Shifts in Person Re-Identification: A Survey and Analysis

Tackling Domain Shifts in Person Re-Identification: A Survey...

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

作者： Vuong D. Nguyen Samiha Mirza Abdollah Zakeri Ayush Gupta Khadija Khaldi Rahma Aloui Pranav Mantini Shishir K. Shah Fatima Merchant University of Houston Johns Hopkins University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The necessity for a Person ReID system for rapidly evolving urban surveillance applications is severely challenged by domain shifts—variations in data distribution that occur across different environments or times. In this paper, we provide the first empirical review of domain shift in person ReID, which includes three settings namely Unsupervised Domain Adaptation ReID, Domain Generalizable ReID, and Lifelong ReID. We observe that existing approaches only tackle domain shifts caused by cross-dataset setting, while ignoring intra-dataset attribute domain shifts caused by changes in clothing, shape, or gait, which is very common in ReID. Thus, we enhance research directions in this field by redefining domain shift in ReID as the combination of attribute domain shift with cross-dataset domain shift. With a focus on Lifelong Re-ID methods, we conduct an extensive comparison on a fair experimental setup and provide an in-depth analysis of these methods under both non-cloth-changing and cloth-changing Re-ID scenarios. Insights into the strengths and limitations of these methods based on their performance are studied. This paper outlines future research directions and paves the way for the development of more adaptive, resilient, and enduring cross-domain ReID systems. Code is available here.

关键词： Surveys computer vision Codes Shape Reviews Surveillance conferences

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QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

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

引用

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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Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

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

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and inter-modal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https://***/praveena2j/RJCMA.

关键词： Correlation coefficient Visualization Emotion recognition computer vision Computational modeling conferences Refining

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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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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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Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

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

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City University of Technology VNU-HCM Vietnam Vietnam National University Ho Chi Minh City University of Science VNU-HCM Vietnam University of Information Technology VNU-HCM Vietnam FPT Telecom Vietnam AI Lab - AI Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty-2024-Track2

关键词： Training Pedestrians Source coding Urban areas Data preprocessing Transportation Data models

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Cache and Reuse: Rethinking the Efficiency of On-device Transfer Learning

Cache and Reuse: Rethinking the Efficiency of On-device Tran...

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

作者： Yuedong Yang Hung-Yueh Chiang Guihong Li Diana Marculescu Radu Marculescu Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Training only the last few layers in deep neural networks has been considered an effective strategy for enhancing the efficiency of on-device training. Prior work has adopted this approach and focused on accelerating backpropagation. However, by conducting a thorough system-wide analysis, we discover that the primary bottleneck is actually the forward propagation through the frozen layers, rather than backpropagation, if only the last few layers are trained. To address this issue, we introduce the "cache and reuse" idea for on-device transfer learning and propose a two-stage training method, which consists of a cache initialization stage, where we store the output from the frozen layers, followed by a training stage. To make our approach practical, we also propose augmented feature caching and cache compression to address the challenges of non-cacheable feature maps and cache size explosion. We carry out extensive experiments on various models (e.g., convolutional neural network and vision transformers) using real edge devices to demonstrate the effectiveness of our method. As an example, on NVIDIA Jetson Orin NX with MobileNet-V2, our approach boosts the training speed by 6.6 ×, and improves the accuracy by 2.1%. For EfficientNet-b0, our method increases the training speed by 2.2 × and improves its accuracy by 1.3%. Therefore, our approach represents a significant improvement in enabling practical on-device transfer learning for edge devices with limited resources.

关键词： Training Backpropagation computer vision Accuracy conferences Transfer learning Transformers

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Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution

Multi-Scale Feature Fusion using Channel Transformers for Gu...

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

作者： Raghunath Sai Puttagunta Birendra Kathariya Zhu Li George York University of Missouri-Kansas City US Air Force Academy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low- light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution. We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal Image Super-Resolution (GTISR).GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs. At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale *** overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs. MSFFCT addresses these shortcomings by enabling efficient processing of global information and offering superior scalability. MSFFCT achieved state-of-the-art results on the ×8 and × 16 GTISR tasks of the 2024 Perception Beyond Visual Spectrum (PBVS) challenge, winning 2 nd place in both tasks and demonstrating its effectiveness in real-world scenarios.

关键词： computer vision Visualization Limiting Superresolution Imaging Transformer cores Transformers

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Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

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

作者： Fabian Perez Hoover Rueda-Chacón Universidad Industrial de Santander

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/Spectral-material-segmentation

关键词： Deep learning Training Image segmentation computer vision Visualization Three-dimensional displays Lighting

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