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A Review and Efficient Implementation of Scene Graph Generation Metrics

A Review and Efficient Implementation of Scene Graph Generat...

引用

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

作者： Julian Lorenz Robin Schön Katja Ludwig Rainer Lienhart University of Augsburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metrics used to evaluate scene graph generation models are lacking. In this paper, we address this gap in the literature by providing a review and precise definition of commonly used metrics in scene graph generation. Our comprehensive examination clarifies the underlying principles of these metrics and can serve as a reference or introduction to scene graph ***, to facilitate the usage of these metrics, we introduce a standalone Python package called SGBench that efficiently implements all defined metrics, ensuring their accessibility to the research community. Additionally, we present a scene graph benchmarking web service, that enables researchers to compare scene graph generation methods and increase visibility of new methods in a central *** of our code can be found under https://***/sgbench/.

关键词： Measurement computer vision Solid modeling Reviews Web services conferences Computational modeling

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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

引用

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

作者： Raz Ramon Hadar Cohen-Duwek Elishai Ezra Tsur Department of Mathematics and Computer Science Neuro-Biomorphic Engineering Lab (NBEL) The Open University of Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBELab/UnsupervisedTracking.

关键词： Training Neuromorphics Computational modeling Machine vision Supervised learning Feature extraction pattern recognition

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Certified Adversarial Robustness Within Multiple Perturbation Bounds

Certified Adversarial Robustness Within Multiple Perturbatio...

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

作者： Nandi, Soumalya Addepalli, Sravanti Rangwani, Harsh Babu, R. Venkatesh Indian Institute of Science Vision and AI Lab Bengaluru India

ISBN: (纸本)9798350302493

Randomized smoothing (RS) is a well known certified defense against adversarial attacks, which creates a smoothed classifier by predicting the most likely class under random noise perturbations of inputs during inference. While initial work focused on robustness to 2 norm perturbations using noise sampled from a Gaussian distribution, subsequent works have shown that different noise distributions can result in robustness to other p norm bounds as well. In general, a specific noise distribution is optimal for defending against a given p norm based attack. In this work, we aim to improve the certified adversarial robustness against multiple perturbation bounds simultaneously. Towards this, we firstly present a novel certification scheme, that effectively combines the certificates obtained using different noise distributions to obtain optimal results against multiple perturbation bounds. We further propose a novel training noise distribution along with a regularized training scheme to improve the certification within both 1 and 2 perturbation norms simultaneously. Contrary to prior works, we compare the certified robustness of different training algorithms across the same natural (clean) accuracy, rather than across fixed noise levels used for training and certification. We also empirically invalidate the argument that training and certifying the classifier with the same amount of noise gives the best results. The proposed approach achieves improvements on the ACR (Average Certified Radius) metric across both 1 and 2 perturbation bounds. Code available at https://***/valiisc/NU-Certified-Robustness © 2023 ieee.

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CroSpace6D: Leveraging Geometric and Motion Cues for High-Precision Cross-Domain 6DoF Pose Estimation for Non-Cooperative Spacecrafts

CroSpace6D: Leveraging Geometric and Motion Cues for High-Pr...

引用

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

作者： Jianhong Zuo Shengyang Zhang Qianyu Zhang Yutao Zhao Baichuan Liu Aodi Wu Xue Wan Leizheng Shu Guohua Kang Nanjing University of Aeronautics and Astronautics Chinese Academy of Sciences Technology and Engineering Center for Space Utilization University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The utilization of monocular vision for non-cooperative spacecraft pose estimation has been significantly researched in space target monitoring, on-orbit servicing, and satellite maintenance. The challenge lies in addressing the cross-domain variations in shape, texture, lighting, and motion patterns between simulated and real captured images. To tackle this issue, a novel domain adaptation 6DoF pose estimation algorithm is proposed to extract the geometric and semantic consistency between cross-domain training and testing datasets. Experimental results demonstrate that our pose estimation method achieves state-of-the-art performance on the SPARK2024 dataset.

关键词： Space vehicles Training Satellites Shape Pose estimation Semantics pattern recognition

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HaLViT: Half of the Weights are Enough

HaLViT: Half of the Weights are Enough

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

作者： Onur Can Koyun Behçet Uğur Töreyin Dept. of Artificial Intelligence and Data Engineering Informatics Institute Signal Processing for Computational Intelligence Research Group (SP4CING) İstanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep learning architectures like Transformers and Convolutional Neural Networks (CNNs) have led to ground-breaking advances across numerous fields. However, their extensive need for parameters poses challenges for implementation in environments with limited resources. In our research, we propose a strategy that focuses on the utilization of the column and row spaces of weight matrices, significantly reducing the number of required model parameters without substantially affecting performance. This technique is applied to both Bottleneck and Attention layers, achieving a notable reduction in parameters with minimal impact on model efficacy. Our proposed model, HaLViT, exemplifies a parameter-efficient vision Transformer. Through rigorous experiments on the ImageNet dataset and COCO dataset, HaLViT’s performance validates the effectiveness of our method, offering results comparable to those of conventional models.

关键词： computer vision conferences Computational modeling Deep architecture Transformers pattern recognition Convolutional neural networks

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Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

Breaking Through the Haze: An Advanced Non-Homogeneous Dehaz...

引用

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

作者： Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Mcmaster University Department of Electrical and Computer Engineering Hamilton Canada University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350302493

Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and. non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and. their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained. ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field, provided, by FFC residual blocks, our model is able to effectively explore global contextual information and. produce images with better perceptual quality In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed, to Res2Net is adopted. This enables our model to learn more supplementary information and. acquire a stronger generalization ability. The feasibility and effectiveness of the proposed, method, is demonstrated, via extensive experiments and. ablation studies. The code is available at https://***/zhouhll5/DWT-FFC. © 2023 ieee.

关键词： Convolution

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Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing

Supervised Contrastive Learning for Snapshot Spectral Imagin...

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

作者： Chuanbiao Song Yan Hong Jun Lan Huijia Zhu Weiqiang Wang Jianfu Zhang Ant Group Qing Yuan Research Institute Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study reveals a cutting-edge re-balanced contrastive learning strategy aimed at strengthening face anti-spoofing capabilities within facial recognition systems, with a focus on countering the challenges posed by printed photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS dataset, which benefits from Snapshot Spectral Imaging technology to provide hyperspectral images, our approach harmonizes class-level contrastive learning with data resampling and an innovative real-face oriented reweighting technique. This method effectively mitigates dataset imbalances and reduces identity-related biases. Notably, our strategy achieved an unprecedented 0.0000% Average Classification Error Rate (ACER) on the HySpeFAS dataset, ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing Challenge on CVPR 2024.

关键词： Training computer vision Error analysis Face recognition conferences Neural networks Imaging

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Multi-view Semantic Information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

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

作者： Li, Yiming Cong, Ruixuan Wang, Sizhe Zhao, Mingyuan Zhang, Yang Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Gaussian Label Distribution Learning for Spherical Image Object Detection

Gaussian Label Distribution Learning for Spherical Image Obj...

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

作者： Xu, Hang Liu, Xinyuan Zhao, Qiang Ma, Yike Yan, Chenggang Dai, Feng Hangzhou Dianzi Univ Hangzhou Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use ln-norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for ln-norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian Label Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach.

关键词： computer vision theory

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SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

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

作者： Faridoun Mehri Mohsen Fayyaz Mahdieh Soleymani Baghshah Mohammad Taher Pilehvar Sharif University of Technology Iran University of Tehran Iran Cardiff University UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-CVPR-2024.

关键词： Training computer vision Animals Aggregates conferences Transformers pattern recognition

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