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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

引用

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

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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Advancing COVID-19 Detection in 3D CT Scans

Advancing COVID-19 Detection in 3D CT Scans

引用

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

作者： Qingqiu Li Runtian Yuan Junlin Hou Jilan Xu Yuejie Zhang Rui Feng Hao Chen School of Academy for Engineering and Technology Fudan University China School of Computer Science Fudan University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt-50 as the strong feature extractor, exploring various pre-trained weights and fine-tuning methods. After a thorough comparison, we initialize our model with CMC v1 pre-trained weights which incorporate COVID-19-specific prior knowledge, and perform Visual Prompt Tuning to reduce the number of training parameters. The superiority of our model is demonstrated through extensive experiments, showing significant improvements in COVID-19 detection performance compared to the baseline model. Among 12 participating teams, our method ranked 4th in the 4th COVID-19 Competition Challenge I with an average Macro F1 Score of 94.24%.

关键词： COVID-19 Training Solid modeling Visualization Three-dimensional displays Computational modeling Computed tomography

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ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

ConceptHash: Interpretable Fine-Grained Hashing via Concept ...

引用

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

作者： Kam Woh Ng Xiatian Zhu Yi-Zhe Song Tao Xiang CVSSP University of Surrey iFlyTek-Surrey Joint Research Centre on Artificial Intelligence Surrey Institute for People-Centred Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are automatically discovered without human annotations. Specifically, we leverage a vision Transformer architecture and introduce concept tokens as visual prompts, along with image patch tokens as model inputs. Each concept is then mapped to a specific sub-code at the model output, providing natural sub-code interpretability. To capture subtle visual differences among highly similar sub-categories (e.g., bird species), we incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes while maintaining semantic alignment. This approach allows us to develop hash codes that exhibit similarity within families of species while remaining distinct from species in other families. Extensive experiments on four fine-grained image retrieval benchmarks demonstrate that ConceptHash outperforms previous methods by a significant margin, offering unique sub-code interpretability as an additional benefit. Code at: https://***/kamwoh/concepthash.

关键词： Visualization computer vision Codes Semantics Image retrieval Manuals Inspection

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Mesh Variational Autoencoders with Edge Contraction Pooling

Mesh Variational Autoencoders with Edge Contraction Pooling

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

作者： Yu-Jie Yuan Yu-Kun Lai Jie Yang Qi Duan Hongbo Fu Lin Gao Institute of Computing Technology CAS Beijing Key Laboratory of Mobile Computing and Pervasive Device School of Computer Science and Informatics Cardiff University UK SenseTime Research City University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder (VAE) structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces and perform 3D shape generation. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation and shape interpolation.

关键词： Three-dimensional displays Shape Strain Interpolation Neural networks Machine learning

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Analyzing U-Net Robustness for Single Cell Nucleus Segmentation from Phase Contrast Images

Analyzing U-Net Robustness for Single Cell Nucleus Segmentat...

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

作者： Chenyi Ling Michael Halter Anne Plant Michael Majurski Jeffrey Stinson Joe Chalfoun Software and Systems Division Information Technology Lab NIST Gaithersburg MD Biosystems and Biomaterials Division Material Measurement Lab NIST Gaithersburg MD

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We quantify the robustness of the semantic segmentation model U-Net, applied to single cell nuclei detection, with respect to the following factors: (1) automated vs manual training annotations, (2) quantity of training data, and (3) microscope image focus. The difficulty of obtaining sufficient volumes of accurate manually annotated training data to create an accurate Convolutional Neural Networks (CNN) model is overcome by the temporary use of fluorescent labels to automate the creation of training datasets using traditional image processing algorithms. The accuracy measurement is computed with respect to manually annotated masks which were also created to evaluate the effectiveness of using automated training set generation via the fluorescent images. The metric to compute the accuracy is the false positive/negative rate of cell nuclei detection. The goal is to maximize the true positive rate while minimizing the false positive rate. We found that automated segmentation of fluorescently labeled nuclei provides viable training data without the need for manual segmentation. A training dataset size of four large stitched images with medium cell density was enough to reach a true positive rate above 88% and a false positive rate below 20%.

关键词： Image segmentation Training computer architecture Microprocessors Microscopy Manuals

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Fast Hardware-Aware Neural Architecture Search

Fast Hardware-Aware Neural Architecture Search

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

作者： Li Lyna Zhang Yuqing Yang Yuhang Jiang Wenwu Zhu Yunxin Liu Microsoft Research Tsinghua University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.

关键词： Hardware computer architecture Graphics processing units Hurricanes Training Measurement

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Masked Autoencoders are Secretly Efficient Learners

Masked Autoencoders are Secretly Efficient Learners

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

作者： Zihao Wei Chen Wei Jieru Mei Yutong Bai Zeyu Wang Xianhang Li Hongru Zhu Huiyu Wang Alan Yuille Yuyin Zhou Cihang Xie University of Michigan Ann Arbor Johns Hopkins University UC Santa Cruz Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper provides an efficiency study of training Masked Autoencoders (MAE), a framework introduced by He et al. [13] for pre-training vision Transformers (ViTs). Our results surprisingly reveal that MAE can learn at a faster speed and with fewer training samples while maintaining high performance. To accelerate its training, our changes are simple and straightforward: in the pre-training stage, we aggressively increase the masking ratio, decrease the number of training epochs, and reduce the decoder depth to lower the pre-training cost; in the fine-tuning stage, we demonstrate that layer-wise learning rate decay plays a vital role in unlocking the full potential of pre-trained models. Under this setup, we further verify the sample efficiency of MAE: training MAE is hardly affected even when using only 20% of the original training *** combining these strategies, we are able to accelerate MAE pre-training by a factor of 82 or more, with little performance drop. For example, we are able to pre-train a ViT-B in ~9 hours using a single NVIDIA A100 GPU and achieve 82.9% top-1 accuracy on the downstream ImageNet classification task. Additionally, we also verify the speed acceleration on another MAE extension, SupMAE.

关键词： Training computer vision Costs conferences Computational modeling Graphics processing units Transformers

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Understanding ReLU Network Robustness Through Test Set Certification Performance

Understanding ReLU Network Robustness Through Test Set Certi...

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

作者： Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To ensure networks are used safely, robustness certificates offer formal assurances about the stability of their predictions in a pre-defined range around the input. However, the relationship between correctness and certified robustness remains unclear. In this work, we investigate the unexpected outcomes of verification methods applied to piecewise linear classifiers for clean, perturbed, in- and out-of-distribution samples. In our experiments focused on image classification, we observed that introducing a modest stability margin around the input sample leads to an important reduction in misclassified samples — approximately a 75% decrease — compared to the roughly 11% for samples that are correctly classified. This finding emphasizes the value of formal verification methods as an extra layer of safety, illustrating their effectiveness in enhancing accuracy for data that falls within the distribution. On the other hand, we provide a theoretical demonstration that formal verification methods robustly certify samples sufficiently far from the training distribution. These results are integrated with an experimental analysis and demonstrate their limitations compared to standard out-of-distribution detection methods.

关键词： Accuracy Perturbation methods Neural networks Reliability theory Robustness Stability analysis Safety

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Attention Guidance Distillation Network for Efficient Image Super-Resolution

Attention Guidance Distillation Network for Efficient Image ...

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

作者： Hongyuan Wang Ziyan Wei Qingting Tang Shuli Cheng Liejun Wang Yongming Li School of Computer Science and Technology Xinjiang University Ürümqi China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past decade, neural network-based super-resolution techniques have been developed on a large scale with impressive achievements. Many novel solutions have been proposed, among which lightweight solutions based on convolutional neural networks have been designed for applications in edge devices. To better realize this application, we propose a more lightweight attention guidance distillation network (AGDN). We design the attention guidance distillation block (AGDB) with more efficient space, channel and self-attention as the infrastructure of AGDN. Specifically, multi-level variance-aware spatial attention (MVSA) is designed to better capture structurally information-rich regions with new multi-scale convolution and local variance alignment. Reallocated contrast-aware channel attention (RCCA) is designed to enhance the processing of common information in all channels while redistributing weights across channels. Sparse global self-attention (SGSA) is introduced for selecting the most useful similarity values for image reconstruction. Extensive experiments demonstrate that AGDN strikes a better balance between performance and complexity compared to other models, achieving SOTA performance on several benchmark tests. In addition, our AGDN-S ranks first in the FLOPs track and second in the Parameters track of the NTIRE 2024 Efficient SR Challenge. The code is available at https://***/daydreamer2024/AGDN.

关键词： Runtime Limiting Convolution Superresolution Feature extraction pattern recognition Image restoration

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Robust Data Augmentation and Ensemble Method for Object Detection in Fisheye Camera Images

Robust Data Augmentation and Ensemble Method for Object Dete...

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

作者： Viet Hung Duong Duc Quyen Nguyen Thien Van Luong Huan Vu Tien Cuong Nguyen VNPT Group VNPT AI Hanoi Vietnam Faculty of Computer Science Phenikaa University Hanoi Vietnam University of Transport and Communications Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, traffic surveillance systems have begun leveraging fisheye lenses to minimize the requisite number of cameras for comprehensive coverage of streets and intersections. However, as fisheye images have large radial distortion, they pose new challenges to standard object detection algorithms. In this study, we propose a robust object detection method in traffic scenarios using fisheye cameras. Specifically, we develop a novel data augmentation method, which is applied to VisDrone dataset. Note that we select this dataset for augmentation, since it bears resemblances to the Fisheye8K dataset. Furthermore, we leverage pseudo labels generated by a pre-trained object detection model based on the Fisheye8K and original VisDrone dataset to further enrich the training data. Finally, we utilize various state-of-the-art object detection models trained with different combinations of the proposed augmented data, which are then combined with robust ensemble techniques to further enhance the overall object detection performance. As a result, our proposed method achieves a final F1 score of 64.06% on the 2024 AI City Challenge - Track 4 and ranks first among the competing teams.

关键词： Surveillance Urban areas Training data Object detection Traffic control Data augmentation Cameras

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