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Selective Multi-View Deep Model for 3D Object Classification

Selective Multi-View Deep Model for 3D Object Classification

引用

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

作者： Alzahrani, Mona Usman, Muhammad Anwar, Saeed Helmy, Tarek KFUPM Dept Informat & Comp Sci Dhahran Saudi Arabia Jouf Univ Coll Comp & Informat Sci Sakaka Saudi Arabia KFUPM SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran Saudi Arabia KFUPM Ctr Intelligent Secure Syst Dhahran Saudi Arabia

ISBN: (纸本)9798350365474

3D object classification has emerged as a practical technology with applications in various domains, such as medical image analysis, automated driving, intelligent robots, and crowd surveillance. Among the different approaches, multi-view representations for 3D object classification have shown the most promising results, achieving state-of-theart performance. However, there are certain limitations in current view-based 3D object classification methods. One observation is that using all captured views for classification can confuse the classifier and lead to misleading results for certain classes. Additionally, some views may contain more discriminative information for object classification than others. These observations motivate the development of smarter and more efficient selective multi-view classification models. In this work, we propose a Selective MultiView Deep Model that extracts multi-view images from 3D data representations and selects the most influential view by assigning importance scores using the cosine similarity method based on visual features detected by a pre-trained CNN. The proposed method is evaluated on the ModelNet40 dataset for the task of 3D classification. The results demonstrate that the proposed model achieves an overall accuracy of 88.13% using only a single view when employing a shading technique for rendering the views, pre-trained ResNet152 as the backbone CNN for feature extraction, and a Fully Connected Network (FCN) as the classifier.

关键词： 3D Object Classification 3D Object recognition Multi-View Conventional Neural Network Multi-View Object Classification

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NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mouse-to-Human Domain Transfer

NOISe: Nuclei-Aware Osteoclast Instance Segmentation for Mou...

引用

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

作者： Manne, Sai Kumar Reddy Martin, Brendan Roy, Tyler Neilson, Ryan Peters, Rebecca Chillara, Meghana Lary, Christine W. Motyl, Katherine J. Wan, Michael Northeastern Univ Boston MA 02115 USA MaineHlth Inst Res Scarborough ME USA Univ Maine Orono ME 04469 USA Tufts Univ Sch Med Medford MA 02155 USA

ISBN: (纸本)9798350365474

Osteoclast cell image analysis plays a key role in osteoporosis research, but it typically involves extensive manual image processing and hand annotations by a trained expert. In the last few years, a handful of machine learning approaches for osteoclast image analysis have been developed, but none have addressed the full instance segmentation task required to produce the same output as that of the human expert led process. Furthermore, none of the prior, fully automated algorithms have publicly available code, pretrained models, or annotated datasets, inhibiting reproduction and extension of their work. We present a new dataset with similar to 2 x 10(5) expert annotated mouse osteoclast masks, together with a deep learning instance segmentation method which works for both in vitro mouse osteoclast cells on plastic tissue culture plates and human osteoclast cells on bone chips. To our knowledge, this is the first work to automate the full osteoclast instance segmentation task. Our method achieves a performance of 0.82 mAP(0.5) (mean average precision at intersection-over-union threshold of 0.5) in cross validation for mouse osteoclasts. We present a novel nuclei-aware osteoclast instance segmentation training strategy (NOISe) based on the unique biology of osteoclasts, to improve the model's generalizability and boost the mAP(0.5) from 0.60 to 0.82 on human osteoclasts. We publish our annotated mouse osteoclast image dataset, instance segmentation models, and code at ***/michaelwwan/noise to enable reproducibility and to provide a public tool to accelerate osteoporosis research(1).

关键词： computer vision instance segmentation medical imaging microscope image analysis osteoporosis

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Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained vision Transformers

Calibrating Higher-Order Statistics for Few-Shot Class-Incre...

引用

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

作者： Goswami, Dipam Twardowski, Bartlomiej van de Weijer, Joost Univ Autonoma Barcelona Dept Comp Sci Barcelona Spain Comp Vis Ctr Barcelona Spain IDEAS NCBR Warsaw Poland

ISBN: (纸本)9798350365474

Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from very few data (5 samples) without forgetting the previously learned classes. Recent works in many-shot CIL (MSCIL) (using all available training data) exploited pre-trained models to reduce forgetting and achieve better plasticity. In a similar fashion, we use ViT models pre-trained on large-scale datasets for few-shot settings, which face the critical issue of low plasticity. FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards. While the focus of most recent studies is on how to learn the many-shot first task so that the model generalizes to all future few-shot tasks, we explore in this work how to better model the few-shot data using pre-trained models, irrespective of how the first task is trained. Inspired by recent works in MSCIL, we explore how using higher-order feature statistics can influence the classification of few-shot classes. We identify the main challenge of obtaining a good covariance matrix from few-shot data and propose to calibrate the covariance matrix for new classes based on semantic similarity to the many-shot base classes. Using the calibrated feature statistics in combination with existing methods significantly improves few-shot continual classification on several FSCIL benchmarks. Code is available at https://***/dipamgoswami/FSCIL-Calibration.

关键词： Continual Learning Few-Shot Class-Incremental Learning

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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

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

作者： Mei, Shibin Zhao, Chenglong Yuan, Shengchao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9798350301298

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

关键词： Datasets and evaluation

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NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessm...

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

作者： Li, Xin Yuan, Kun Pei, Yajing Lu, Yiting Sun, Ming Zhou, Chao Chen, Zhibo Timofte, Radu Sun, Wei Wu, Haoning Zhang, Zicheng Jia, Jun Zhang, Zhichao Cao, Linhan Chen, Qiubo Min, Xiongkuo Lin, Weisi Zhai, Guangtao Sun, Jianhui Wang, Tianyi Li, Lei Kong, Han Wang, Wenxuan Li, Bing Luo, Cheng Wang, Haiqiang Chen, Xiangguang Meng, Wenhui Pan, Xiang Shi, Huiying Zhu, Han Xu, Xiaozhong Sun, Lei Chen, Zhenzhong Liu, Shan Kong, Fangyuan Fan, Haotian Xu, Yifang Xu, Haoran Yang, Mengduo Zhou, Jie Li, Jiaze Wen, Shijie Xu, Mai Li, Da Yao, Shunyu Du, Jiazhi Zuo, Wangmeng Li, Zhibo He, Shuai Ming, Anlong Fu, Huiyuan Ma, Huadong Wu, Yong Xue, Fie Zhao, Guozhi Du, Lina Guo, Jie Zhang, Yu Zheng, Huimin Chen, Junhao Liu, Yue Zhou, Dulan Xu, Kele Xu, Qisheng Sun, Tao Ding, Zhixiang Hu, Yuhang Univ Sci & Technol China Hefei Peoples R China Kuaishou Technol Beijing Peoples R China Univ Wurzburg Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China Nanyang Technol Univ Singapore Singapore Xiaohongshu Shanghai Peoples R China WeChat Beijing Peoples R China Tencent Shenzhen Peoples R China Wuhan Univ Wuhan Peoples R China ByteDance Shenzhen Peoples R China Zhejiang Univ Hangzhou Peoples R China Beihang Univ Dept Elect & Informat Engn Beijing Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China China Merchants Bank Shenzhen Peoples R China Shandong Jianzhu Univ Jinan Peoples R China Key Lab Parallel & Distributed Proc Changsha Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350365474

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-cvpr- NTIRE2024.

关键词： HTTP

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Initialization Noise in Image Gradients and Saliency Maps

Initialization Noise in Image Gradients and Saliency Maps

引用

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

作者： Woerl, Ann-Christin Disselhoff, Jan Wand, Michael Johannes Gutenberg Univ Mainz Inst Comp Sci Mainz Germany

ISBN: (纸本)9798350301298

In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration.

关键词： Explainable computer vision

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Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection

Humans as Light Bulbs: 3D Human Reconstruction from Thermal ...

引用

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

作者： Liu, Ruoshi Vondrick, Carl Columbia Univ New York NY 10027 USA

ISBN: (纸本)9798350301298

The relatively hot temperature of the human body causes people to turn into long-wave infrared light sources. Since this emitted light has a larger wavelength than visible light, many surfaces in typical scenes act as infrared mirrors with strong specular reflections. We exploit the thermal reflections of a person onto objects in order to locate their position and reconstruct their pose, even if they are not visible to a normal camera. We propose an analysis-by-synthesis framework that jointly models the objects, people, and their thermal reflections, which combines generative models with differentiable rendering of reflections. Quantitative and qualitative experiments show our approach works in highly challenging cases, such as with curved mirrors or when the person is completely unseen by a normal camera.

关键词： vision + graphics

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NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results

NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods...

引用

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

作者： Chen, Zheng Wu, Zongwei Zamfir, Eduard Zhang, Kai Zhang, Yulun Timofte, Radu Yang, Xiaokang Yu, Hongyuan Wan, Cheng Hong, Yuxin Huang, Zhijuan Zou, Yajun Huang, Yuan Lin, Jiamin Han, Bingnan Guan, Xianyu Yu, Yongsheng Zhang, Daoan Yin, Xuanwu Zuo, Kunlong Hao, Jinhua Zhao, Kai Yuan, Kun Sun, Ming Zhou, Chao An, Hongyu Zhang, Xinfeng Song, Zhiyuan Dong, Ziyue Zhao, Qing Xu, Xiaogang Wei, Pengxu Dou, Zhi-chao Wang, Gui-ling Hsu, Chih-Chung Lee, Chia-Ming Chou, Yi-Shiuan Korkmaz, Cansu Tekalp, A. Murat Wei, Yubin Yan, Xiaole Li, Binren Chen, Haonan Zhang, Siqi Chen, Sihan Joshi, Amogh Akalwadi, Nikhil Malagi, Sampada Yashaswini, Palani Desai, Chaitra Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Sarvaiya, Anjali Choksy, Pooja Joshi, Jagrit Kawa, Shubh Upla, Kishor Patwardhan, Sushrut Ramachandra, Raghavendra Hossain, Sadat Park, Geongi Uddin, S. M. Nadim Xu, Hao Guo, Yanhui Urumbekov, Aman Yan, Xingzhuo Hao, Wei Fu, Minghan Orais, Isaac Smith, Samuel Liu, Ying Jia, Wangwang Xu, Qisheng Xu, Kele Yuan, Weijun Li, Zhan Kuang, Wenqin Guan, Ruijin Deng, Ruting Zhang, Zhao Wang, Bo Zhao, Suiyi Luo, Yan Wei, Yanyan Khan, Asif Hussain Micheloni, Christian Martinel, Niki Xiamen Univ Xiamen Peoples R China

ISBN: (纸本)9798350365474

This paper reviews the NTIRE 2024 challenge on image super-resolution (x4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field.

关键词： image SR NTIRE 2024 challenge

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Spectroformer: Multi-Domain Query Cascaded Transformer Network For Underwater Image Enhancement

Spectroformer: Multi-Domain Query Cascaded Transformer Netwo...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Khan, M. D. Raqib Mishra, Priyanka Mehta, Nancy Phutke, Shruti S. Vipparthi, Santosh Kumar Nandi, Sukumar Murala, Subrahmanyam Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland Indian Inst Technol Ropar CVPR Lab Ropar India Univ Wurzburg Comp Vis Lab IFI CAIDAS Wurzburg Germany Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld Australia Indian Inst Technol Guwahati Gauhati India

ISBN: (纸本)9798350318920;9798350318937

Underwater images often suffer from color distortion, haze, and limited visibility due to light refraction and absorption in water. These challenges significantly impact autonomous underwater vehicle applications, necessitating efficient image enhancement techniques. To address these challenges, we propose a Multi-Domain Query Cascaded Transformer Network for underwater image enhancement. Our approach includes a novel Multi-Domain Query Cascaded Attention mechanism that integrates localized transmission features and global illumination features. To improve feature propagation from the encoder to the decoder, we propose a Spatio-Spectro Fusion-Based Attention Block. Additionally, we introduce a Hybrid Fourier-Spatial Upsampling Block, which uniquely combines Fourier and spatial upsampling techniques to enhance feature resolution effectively. We evaluate our method on benchmark synthetic and real-world underwater image datasets, demonstrating its superiority through extensive ablation studies and comparative analysis. The testing code is available at: https://***/Mdraqibkhan/Spectroformer.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

引用

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

作者： Parisot, Sarah Yang, Yongxin McDonagh, Steven Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

关键词： language reasoning vision

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